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Thrust online fusion estimation of high-flow dual variable cycle engine

AEROSPACE SCIENCE AND TECHNOLOGY 2024年 152卷

作者： Ma, Ansheng Zhou, Xin Zou, Zelong Huang, Jinquan Lu, Feng Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

For the aeroengines, thrust is a crucial performance parameter which is closely related to the mission accessibility. Accurate thrust control is conducive to excavate the potential of the variable cycle engine (VCE) in different operation modes, which meets the requirements of future engine control system. However, the thrust is unmeasured in flight. Besides, the traditional thrust estimation method is difficult to meet the wide-area thrust control requirements due to the strong nonlinearity and large flight envelope for a neoconfiguration VCE of the high-flow triple-bypass, named high-flow dual variable cycle engine (HDVCE). This paper proposes a novel online fusion thrust estimation method mainly under single operation mode for the HDVCE. The problem of individual engine differences is also focused, and the combination of model and data-driven is to generate the thrust estimation with fusion strategy. At first, a multi-combustion chamber coupled dynamic model is established for the HDVCE, and an adaptive network is used for model optimization to improve the model accuracy. On this basis, an EKF-based thrust estimator is built. Secondly, a data-driven thrust estimator is pre-trained by a stacked autoencoder (SAE) and then tuned by back propagation (BP) neural network. Subsequently, the thrust integration is incorporated based on estimation results from the EKF-based and data-driven one using Euclidean distance fusion strategy. Simulation results show that the fusion estimation method proposed in this paper, on the one hand, reduces the effect of model instability on EKF. On the other hand, it ensures the estimation accuracy of SAE-BP far from the training point. The comparisons also reveal that the fusion one produces more than 97% in thrust estimation accuracy and is better than the remaining ones, which is promising for thrust control applications.

关键词： High-flow dual variable cycle engine Thrust fusion estimation Extended Kalman Filter stacked autoencoder Euclidean distance

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Energy-and trust-aware secure routing algorithm for big data classification using MapReduce framework in IoT networks

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第2期12卷 80-103页

作者： S.Md.Mujeeb R.Praveen Sam K.Madhavi Department of CSE Jawaharlal Nehru Technological University Anantapur(JNTUA)Ananthapuramu-515002 Andhra PradeshIndia Department of Computer Science and Engineering G.Pulla Reddy Engineering College(Autonomous)Nandyal Road Kurnool 518007Andhra PradeshIndia

Due to the open nature of wireless data transmission,routing and data security pose an important research challenge in the Internet of Things(IoT)-enabled ***,the characteristic features,like constrained resources,heterogeneity,uncontrolled environment,and scalability requirement,make the security issues even more ***,an effective and secure routing protocol named modified Energy Harvesting Trust-aware Routing Algorithm(mod-EHTARA)is proposed to increase the energy efficiency and the lifespan of the *** proposed mod-EHTARA is designed by adopting the Link Lifetime(LLT)model with the traditional *** optimal secure routing path is effectively selected by the proposed mod-EHTARA using the cost metric,which considers the factors like delay,LLT,energy,and *** big data classification process is carried out at the Base Station(BS)using the MapReduce ***,the big data classification is progressed using a stacked autoencoder,which is trained by the Adaptive E-Bat *** Adaptive E-Bat algorithm is developed by integrating the adaptive concept with the Bat Algorithm(BA)and Exponential Weighted Moving Average(EWMA).The proposed mod-EHTARA showed better performance by obtaining a maximal energy of 0.9855.

关键词： stacked autoencoder big data classification Internet of Things secure routing energy harvesting

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Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

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NEUROIMAGE 2016年第PartA期124卷 127-146页

作者： Kim, Junghoe Calhoun, Vince D. Shim, Eunsoo Lee, Jong-Hwan Korea Univ Dept Brain & Cognit Engn Seoul 136713 South Korea Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Samsung Elect Samsung Adv Inst Technol Suwon South Korea Mind Res Network Albuquerque NM USA LBERI Albuquerque NM USA

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of frame wise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantifie

关键词： Deep learning Functional connectivity Resting-state functional magnetic resonance imaging Schizophrenia Sparsity stacked autoencoder

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Data-driven inverse modeling with a pre-trained neural network at heterogeneous channel reservoirs

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2018年 170卷 785-796页

作者： Ahn, Seongin Park, Changhyup Kim, Jaejun Kang, Joe M. Samsung Heavy Ind Dept Energy Plant Researching Seongnam 13486 Gyeonggi South Korea Kangwon Natl Univ Dept Energy & Resources Engn Chunchon 24341 Kangwon South Korea Seoul Natl Univ Dept Energy Syst Engn Seoul 08826 South Korea

This paper develops a reliable and efficient data-integration method, based on artificial neural networks (ANN) incorporated with a stacked autoencoder (SAE) in a deep neural network's framework. To handle scale-different static and dynamic data of heterogeneous channel reservoirs, the workflow suggests an unsupervised pretraining process coupled with ANN-based inverse modeling. The performances of the proposed neural network, i.e. the training efficiency, the predictability of future production rates and the computing time, are compared to those with an optimal ANN and the impact of hidden neurons are discussed. The pre-trained neural network demonstrates a reliable estimation of reservoir properties with the spatial characteristics of a true channel reservoir while the ANN fails with respect to the spatial heterogeneity. The pre-trained neural network decreases the mean absolute error of future oil production rates up to 9.1% which is less than 25% of the comparison case's level, i.e. the optimal ANN model. Its efficiency is validated by a computing time 14 times faster than that of the optimal ANN workflow. The pre-trained neural network evaluates the spatial characteristics of reservoir properties and facies models in a reasonable manner and is thus able to predict the water production rates and also the breakthrough time accurately. This pre-trained neural network manifests its applicability with robustness as an efficient method to integrate static and dynamic data.

关键词： stacked autoencoder Deep neural network Pretraining Inverse model Data integration

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Lightweight 3D Dense autoencoder Network for Hyperspectral Remote Sensing Image Classification

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SENSORS 2023年第20期23卷 8635-8635页

作者： Bai, Yang Sun, Xiyan Ji, Yuanfa Fu, Wentao Duan, Xiaoyu Guilin Univ Elect Technol Informat & Commun Schnool Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Natl & Local Joint Engn Res Ctr Satellite Nav Posi Guilin 541004 Peoples R China

The lack of labeled training samples restricts the improvement of Hyperspectral Remote Sensing Image (HRSI) classification accuracy based on deep learning methods. In order to improve the HRSI classification accuracy when there are few training samples, a Lightweight 3D Dense autoencoder Network (L3DDAN) is proposed. Structurally, the L3DDAN is designed as a stacked autoencoder which consists of an encoder and a decoder. The encoder is a hybrid combination of 3D convolutional operations and 3D dense block for extracting deep features from raw data. The decoder composed of 3D deconvolution operations is designed to reconstruct data. The L3DDAN is trained by unsupervised learning without labeled samples and supervised learning with a small number of labeled samples, successively. The network composed of the fine-tuned encoder and trained classifier is used for classification tasks. The extensive comparative experiments on three benchmark HRSI datasets demonstrate that the proposed framework with fewer trainable parameters can maintain superior performance to the other eight state-of-the-art algorithms when there are only a few training samples. The proposed L3DDAN can be applied to HRSI classification tasks, such as vegetation classification. Future work mainly focuses on training time reduction and applications on more real-world datasets.

关键词： hyperspectral remote sensing image classification deep learning dense connection stacked autoencoder

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LaMeta: An efficient locality aware metadata management technique for an ultra-large distributed storage system

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 8323-8335页

作者： Singh, Harcharan Jit Bawa, Seema Thapar Inst Engn & Technol Deemed Univ Dept Comp Sci & Engn Patiala 147004 Punjab India

The locality of metadata is a critical parameter of data storage and retrieval performance for an ultra -large distributed storage system. The data of an ultra-large distributed storage system is accessed through its distributed metadata servers (MDSs). The distributed MDSs store and cache partial metadata for scalability and performance. The MDSs are connected through varied latencies multipath network connections of using various metadata distribution techniques. The locality of MDS in the metadata dis-tribution techniques improves the metadata query routing and enhances the performance of the dis-tributed storage system. Motivated by this insight, this work introduces an intelligent locality-aware metadata management technique based on recommendation system that exploits MDS path ratings in a globally distributed ultra-large storage system and proposes a novel locality-aware metadata query routing algorithm. The proposed metadata server locality aware employs collaborative filtering with stacked autoencoder to address the sparsity of the path rating matrix and its overfitting issue. The exten-sive experiments on real datasets show that the proposed metadata server locality aware technique enhances the locality of metadata operations and improves aggregate operations throughput by 12% to 21% of contemporary distributed metadata management techniques in ultra-large distributed storage systems. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Deep learning stacked autoencoder Machine learning High performance computing Distributed metadata management Locality Storage system

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UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

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Journal of Systems Engineering and Electronics 2023年第5期34卷 1235-1251页

作者： XIE Lei TANG Shangqin WEI Zhenglei XUAN Yongbo WANG Xiaofei Institute of Aeronautics Engineering Air Force Engineering UniversityXi’an 710038China China Aerodynamics Research&Development Center Mianyang 621000China Blue Sky Innovation Center for Frontier Science Beijing 100000China

The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of *** overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is *** clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid *** evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is *** convolutional pooling layer is used to compress the size of data and preserve the distribution *** LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization *** experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient *** with the five kinds of assess-ments models,the SAE-LVQ model has the highest ***,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation *** assessment results are in line with the actual situation.

关键词： unmanned combat aerial vehicle(UCAV) situation assessment clustering K-means stacked autoencoder learn-ing vector quantization

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On deep ensemble CNN-SAE based novel agro-market price forecasting

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EVOLUTIONARY INTELLIGENCE 2021年第2期14卷 851-862页

作者： Parida, Nirjharinee Mishra, Debahuti Das, Kaberi Rout, Narendra Kumar Panda, Ganapati Siksha O Anusandhan Deemed Univ Dept Comp Sci Engn Bhubaneswar Odisha India Natl Inst Technol Dept Comp Applicat GE Rd Chhatisgarh 492010 CG India CV Raman Global Univ Dept Elect & Telecommun Engn Bhubaneswar 752054 Odisha India

The prices of agro-commodities are highly volatile. Hence it is a challenge to the farmers to ensure fair and remunerative prices of these commodities. As a result, there is a need for prediction of agro market price appropriately. The closing price prediction of one soft commodity product, Cotton 29 mm and one agro-commodity product, Guar gum are chosen. The existing reported methods exhibit poor prediction performance. To alleviate this problem, the current investigation is undertaken for better prediction of closing prices. The deep ensemble approach using convolutional neural network (CNN) and stacked autoencoder (SAE) is employed to improve the prediction performance. For the ensemble strategy, the weights are optimized using three bio-inspired techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and spider monkey optimization (SMO). Eighteen attributes relating to the closing price of each products are considered as input to the proposed models. The simulation based experimental results demonstrate the following contribution of the paper. Firstly, it is observed that CNN outperforms the SAE model in terms of short range prediction and vice versa for long range prediction. Secondly, the prediction performance of all the three ensemble models has been determined. Thirdly, out of three ensemble models, ensemble-SMO (ESMO) shows the best prediction performance in terms of mean square error and coefficient of multiple determination (R-2). It is then followed by ensemble-PSO and ensemble-GA respectively. The performance of proposed best ESMO is compared with the Grey wolf optimization based multiquadratic kernel KELM model (GWO-KELM) and it is observed that the proposed ESMO outperforms the GWO-KELM model.

关键词： Deep learning Convolutional neural network stacked autoencoder Ensemble learning Spider monkey optimization Genetic algorithm Particle swarm optimization R-2(coefficient of multiple determination)

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Deep Learning Technique-Based Steering of Autonomous Car

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2018年第2期17卷

作者： Yang, Yiqin Wu, Zhe Xu, Qingyang Yan, Fabao Shandong Univ Sch Mech Elect & Informat Engn 180 Wenhua Xilu Weihai 264209 Shandong Peoples R China

Deep neural network (DNN) has many advantages. Autonomous driving has become a popular topic now. In this paper, an improved stack autoencoder based on the deep learning techniques is proposed to learn the driving characteristics of an autonomous car. These techniques realize the input data adjustment and solving diffusion gradient problem. A Raspberry Pi and a camera module are mounted on the top of the car. The camera module provides the images needed for training the DNN. There are two stages in the training. In the pre-training process, an improved autoencoder is trained by the unsupervised learning mechanism, and the characterization of the track is extracted. In the fine-tuning stage, the whole network is trained according to the labeled data, and then this model learns the driving characteristics better according to the samples. In the experimental stage, the car will predict the action of the car by the trained model in the autonomous mode. The experiment exhibits the effectiveness of the proposed model. Compared with the traditional neural network, the improved stack autoencoder has a better generalization ability and faster convergence speed.

关键词： Raspberry Pi batch normalization self-driving car stacked autoencoder deep learning

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NOTE: non-parametric oversampling technique for explainable credit scoring

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Han, Seongil Jung, Haemin Yoo, Paul D. Cali, Andrea Provetti, Alessandro Univ London Birkbeck Coll Sch Comp & Math Sci London England Korea Natl Univ Transportat Dept Ind & Management Engn Chungju South Korea

Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling TEchnique (SMOTE) struggles with high-dimensional, non-linear data and may introduce noise through class overlap. Generative Adversarial Networks (GANs) have emerged as an alternative, offering the ability to model complex data distributions. Conditional Wasserstein GANs (cWGANs) have shown promise in handling both numerical and categorical features in credit scoring datasets. However, research on extracting latent features from non-linear data and improving model explainability remains limited. To address these challenges, this paper introduces the Non-parametric Oversampling Technique for Explainable credit scoring (NOTE). The NOTE offers a unified approach that integrates a Non-parametric stacked autoencoder (NSA) for capturing non-linear latent features, cWGAN for oversampling the minority class, and a classification process designed to enhance explainability. The experimental results demonstrate that NOTE surpasses state-of-the-art oversampling techniques by improving classification accuracy and model stability, particularly in non-linear and imbalanced credit scoring datasets, while also enhancing the explainability of the results.

关键词： Conditional Wasserstein generative adversarial networks stacked autoencoder Explainable AI Imbalanced class Oversampling Credit scoring

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