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hyper-parameter optimization based nonlinear multistate deterioration modeling for deterioration level assessment and remaining useful life prognostics

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2017年 167卷 517-526页

作者： Chen, Gaige Chen, Jinglong Zi, Yanyang Miao, Huihui Xi An Jiao Tong Univ State Key Lab Mfg & Syst Engn Xian Shaanxi Peoples R China

Complex equipment deterioration refers to a nonlinear multistate deterioration process, where the deterioration curve may not follow a typical shape such as exponential or linear function. A general solution is presented to nonlinear multistate deterioration modeling for deterioration level assessment and remaining useful life prognostics under no-label lifetime data including multi signals. In the solution, a three layer nonlinear multistate deterioration model of complex equipment is established based on hyper-parameter optimization. hyper-parameter I and M, which determine the first two layers, are optimized by the proposed unsupervised extraction method based on greedy kernel principal components analysis and the improved Mann-Kendall criterion, respectively. As a determinant of the third layer, hyper-parameter N is optimized by the improved Bayesian information criterion to obtain optimized model, when parameters have been estimated under each alternative model structure at different N. The no-label lifetime dataset of turbofan engines are adopted for case study, and more accurate deterioration level and remaining useful life are obtained. The results verify the effectiveness of the presented solution. The study indicates that: nonlinearity makes an important effect on multistate deterioration modeling through hyper-parameters;the solution deals with nonlinearity in a systematic manner by hyper-parameter optimization of three layers. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Remaining useful life Multistate deterioration Nonlinearity hyper-parameter optimization Complex equipment

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hyper-parameter optimization of Multi-attention Recurrent Neural Network for Battery State-of-Charge Forecasting 19th

Hyper-parameter Optimization of Multi-attention Recurrent Ne...

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19th EPIA Conference on Artificial Intelligence (EPIA)

作者： Mashlakov, Aleksei Tikka, Ville Lensu, Lasse Romanenko, Aleksei Honkapuro, Samuli LUT Univ Yliopistonkatu 34 Lappeenranta 53850 Finland

ISBN: (纸本)9783030302412;9783030302405

In the past years, a rapid deployment of battery energy storage systems for diverse smart grid services has been seen in electric power systems. However, a cost-effective and multi-objective application of these services necessitates a utilization of forecasting methods for a development of efficient capacity allocation and risk management strategies over the uncertainty of battery state-of-charge. The aim of this paper is to assess the tuning efficiency of multi-attention recurrent neural network for multi-step forecasting of battery state-of-charge under provision of primary frequency control. In particular, this paper describes hyperparameter optimization of the network with a tree-structured parzen estimator and compares such optimization performance with random and manual search on a simulated battery state-of-charge dataset. The experimental results demonstrate that the tree-structured parzen estimator enables 0.6% and 1.5% score improvement for the dataset compared with the random and manual search, respectively.

关键词： Battery state-of-charge hyper-parameter optimization Multi-attention neural network Random search Tree parzen estimator

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hyper-parameter optimization for Latent Spaces 1

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21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Veloso, Bruno Caroprese, Luciano Konig, Matthias Teixeira, Sonia Manco, Giuseppe Hoos, Holger H. Gama, Joao Portucalense Univ Porto Portugal Leiden Univ Leiden Netherlands Univ Porto Porto Portugal Univ British Columbia Vancouver BC Canada ICAR CNR Arcavacata Di Rende Italy LIAAD INESC TEC Porto Portugal

ISBN: (数字)9783030865238

ISBN: (纸本)9783030865238;9783030865221

We present an online optimization method for time-evolving data streams that can automatically adapt the hyper-parameters of an embedding model. More specifically, we employ the Nelder-Mead algorithm, which uses a set of heuristics to produce and exploit several potentially good configurations, from which the best one is selected and deployed. This step is repeated whenever the distribution of the data is changing. We evaluate our approach on streams of real-world as well as synthetic data, where the latter is generated in such way that its characteristics change over time (concept drift). Overall, we achieve good performance in terms of accuracy compared to state-of-the-art AutoML techniques.

关键词： AutoML hyper-parameter optimization Latent spaces Nelder-Mead algorithm SMAC Recommender systems

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hyper-parameter optimization OF DEEP CONVOLUTIONAL NETWORKS FOR OBJECT RECOGNITION

HYPER-PARAMETER OPTIMIZATION OF DEEP CONVOLUTIONAL NETWORKS ...

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IEEE International Conference on Image Processing (ICIP)

作者： Talathi, Sachin S. Qualcomm Res Ctr 5775 Morehouse Dr San Diego CA 92121 USA

ISBN: (纸本)9781479983391

Recently sequential model based optimization (SMBO) has emerged as a promising hyper-parameter optimization strategy in machine learning. In this work, we investigate SMBO to identify architecture hyper-parameters of deep convolution networks (DCNs) object recognition. We propose a simple SMBO strategy that starts from a set of random initial DCN architectures to generate new architectures, which on training perform well on a given dataset. Using the proposed SMBO strategy we are able to identify a number of DCN architectures that produce results that are comparable to state-of-the-art results on object recognition benchmarks.

关键词： hyper-parameter optimization deep convolution networks sequential model based optimization

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hyper-parameter optimization FOR CONVOLUTIONAL NEURAL NETWORK COMMITTEES BASED ON EVOLUTIONARY ALGORITHMS 24

HYPER-PARAMETER OPTIMIZATION FOR CONVOLUTIONAL NEURAL NETWOR...

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24th IEEE International Conference on Image Processing (ICIP)

作者： Bochinski, Erik Senst, Tobias Sikora, Thomas Tech Univ Berlin Commun Syst Grp Berlin Germany

ISBN: (纸本)9781509021758

In a broad range of computer vision tasks, convolutional neural networks (CNNs) are one of the most prominent techniques due to their outstanding performance. Yet it is not trivial to find the best performing network structure for a specific application because it is often unclear how the network structure relates to the network accuracy. We propose an evolutionary algorithm-based framework to automatically optimize the CNN structure by means of hyper-parameters. Further, we extend our framework towards a joint optimization of a committee of CNNs to leverage specialization and cooperation among the individual networks. Experimental results show a significant improvement over the state-of-the-art on the well-established MNIST dataset for hand-written digits recognition.

关键词： Image Classification Convolutional Neural Network Evolutionary Algorithm MNIST hyper-parameter optimization

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hyper-parameter optimization for Deep Learning by Surrogate-based Model with Weighted Distance Exploration

Hyper-Parameter Optimization for Deep Learning by Surrogate-...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Li, Zhenhua Shoemaker, Christine A. Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Nanjing Peoples R China Natl Univ Singapore Dept Ind Syst Engn Singapore Singapore

ISBN: (纸本)9781728183923

To improve deep neural net hyper-parameter optimization we develop a deterministic surrogate optimization algorithm as an efficient alternative to Bayesian optimization. A deterministic Radial Basis Function (RBF) surrogate model is built to interpolate previously evaluated points, and this surrogate model is incrementally updated in each iteration. The stochastic algorithm CMA-ES is used to search the acquisition function based on the surrogate. The acquisition function at a point is based on a weighted average of the surrogate at x and the minimum distance from x to a previously evaluated point. We evaluate the proposed algorithm RBF-CMA on hyper-parameter optimization tasks for deep convolutional neural networks on datasets of CIFAR-10, SVHN, and CIFAR-100. We show that RBF-CMA achieves a promising performance especially when the search space dimension is high in comparison to other algorithms including GP-EI, GP-LCB, and SMBO.

关键词： hyper-parameter optimization deep neural networks surrogate optimization radial basis function

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hyper-parameter optimization OF DEEP LEARNING MODELS FOR COMPRESSOR AIR LEAK PREDICTION IN A GAS TURBINE

HYPER-PARAMETER OPTIMIZATION OF DEEP LEARNING MODELS FOR COM...

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ASME Power Conference (Power)

作者： Nogueras-Rivera, Diego I. Mojica-Vazquez, Lemuel Bonilla-Alvarado, Harry Bryden, Kenneth M. Tucker, David Traverso-Aviles, Luis M. Aponte-Roa, Diego A. Univ Ana G Mendez Elect & Comp Engn Dept San Juan PR 00926 USA Ames Lab Decis Sci Program Ames IA USA US DOE Natl Energy Technol Lab Washington DC USA Univ Ana G Mendez Mech Engn Dept San Juan PR USA

ISBN: (纸本)9780791885826

Gas turbine systems are widely used in the power industry because they provide continuous and reliable power to the electrical grid. One of the main concerns for implementing gas turbine systems is the maintenance costs. Therefore, predictive maintenance methods driven by Deep Learning (DL) models present an opportunity to extract important information and knowledge from the process data to minimize maintenance costs and reduce equipment failure rates. A previous study aimed to benchmark various state-of-the-art DL models for predicting compressor air leak with multivariate time-series data from a modified recuperated gas turbine system. However, the brute-force approach used to select the hyper-parameters of the DL models could be improved. This paper aims to address the hyper-parameter optimization process of the best performers for predicting the next future time-step: GRU-LSTM, Sequential CNNLSTM, and BI-LSTM. In addition, a BI-GRU model was implemented and a common grid search algorithm was combined with proposed algorithms for automating the selection of hyper-parameter values to build the DL models. The datasets were provided from experiments conducted at the U.S. Department of Energy's National Energy Technology Laboratory (NETL) Hybrid Performance (hyper) Facility. Results suggest better performance can be obtained from the already good performing benchmarked models;however, reproducing the best results for some models may take more training cycles. The BI-GRU model exhibited the most reproducible results across all tests.

关键词： Deep Learning Failure Prediction Gas Turbines hyper-parameter optimization Time Series

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hyper-parameter optimization for Improving the Performance of Grammatical Evolution

Hyper-Parameter Optimization for Improving the Performance o...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Wang, Hao Lou, Yitan Back, Thomas Leiden Univ LIACS Niels Bohrweg 1 Leiden Netherlands

ISBN: (纸本)9781728121536

State-of-the-art Grammatical Evolution systems such as PonyGE2 have a number of hyper-parameters that control the behavior of the internal evolutionary algorithm for evolving the representations of programs. In this paper, a variant of the efficient global optimization (EGO) algorithm is applied for optimizing these hyper-parameters of the PonyGE2-system. This approach is tested on four test problems used in the Grammatical Evolution community: StringMatch, symbolic regression (the 'Vladislavleva-4' problem), bank note classification and the so-called Pymax task. The experimental results show that the average performance of the GE system is improved significantly (between 25% and 168%) on all of the test problems. In addition, the resulting overall best hyper-parameter settings are substantially different from the defaults used in PonyGE2.

关键词： Grammatical Evolution hyper-parameter optimization

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A new hyper-parameter optimization method for machine learning in fault classification

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APPLIED INTELLIGENCE 2023年第11期53卷 14182-14200页

作者： Ye, Xingchen Gao, Liang Li, Xinyu Wen, Long Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol 1037 Luoyu Rd Wuhan 430074 Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat 388 Lumo Rd Wuhan 430074 Peoples R China

Accurate bearing fault classification is essential for the safe and stable operation of rotating machinery. The success of Machine Learning (ML) in fault classification is mainly dependent on efficient features and the optimal pre-defined hyper-parameters. Various hyper-parameter optimization (HPO) methods have been proposed to tune the ML algorithms' hyper-parameters in low dimensions but ignore the hyper-parameters of Feature Engineering (FE). The hyper-parameter dimension is high because both FE and the ML algorithm contain many hyper-parameters. This paper proposed a new HPO method for high dimensions based on dimension reduction and partial dependencies. Firstly, the whole hyper-parameter space is separated into two subspaces of FE and the ML algorithm to reduce time consumption. Secondly, the sensitive intervals of hyperparameters can be recognized by partial dependencies due to the nonlinearity of the relationship between the hyperparameters. Then HPO is conducted in intervals to acquire more satisfactory accuracy. The proposed method is verified on three OpenML datasets and the CWRU bearing dataset. The results show that it can automatically construct efficient domain features and outperforms traditional HPO methods and famous ML algorithms. The proposed method is also very time efficient.

关键词： hyper-parameter optimization Fault classification Dimension reduction Partial dependencies

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A lightweight intrusion detection system for internet of vehicles based on transfer learning and MobileNetV2 with hyper-parameter optimization

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期83卷 22347-22369页

作者： Wang, Yingqing Qin, Guihe Zou, Mi Liang, Yanhua Wang, Guofeng Wang, Kunpeng Feng, Yao Zhang, Zizhan Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Publ Comp Educ & Res Ctr Changchun 130012 Peoples R China

With the rapid development of Internet of Vehicles (IoV) technology, Intelligent Connected Vehicles (ICVs) have richer vehicle information functions and applications. In recent years, as ICVs have become more complex and intelligent, vehicle information security is facing great threats and challenges. Therefore, it is of great significance to develop efficient intrusion detection methods to protect the information security of IoV. In this paper, after analyzing the vulnerability of intra-vehicle networks (IVNs) and external vehicle networks (EVNs), we propose a lightweight intrusion detection method, which uses MobileNetv2 as the backbone, combines transfer learning (TL) techniques and the hyper-parameter optimization (HPO) method. The proposed method can detect various types of attacks, and the Accuracy, Precision, and Recall on the Car-Hacking dataset representing IVNs data are all 100 %. The Accuracy, Precision, and Recall on the CICIDS2017 dataset representing EVNs data are all 99.93 %. The average processing time of each packet tested is about 0.75 ms, and the model space is 23 M. Experimental results demonstrate that the proposed intrusion detection method is effective and lightweight.

关键词： Intrusion Detection System Transfer Learning Internet of Vehicles MobileNetV2 Lightweight Networks hyper-parameter optimization

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