Research

25 pages, 3314 KiB

Open AccessArticle

Novel GA-Based DNN Architecture for Identifying the Failure Mode with High Accuracy and Analyzing Its Effects on the System

by Naeim Rezaeian, Regina Gurina, Olga A. Saltykova, Lokmane Hezla, Mammetnazar Nohurov and Kazem Reza Kashyzadeh

Appl. Sci. 2024, 14(8), 3354; https://doi.org/10.3390/app14083354 - 16 Apr 2024

Viewed by 245

Abstract

Symmetric data play an effective role in the risk assessment process, and, therefore, integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is essential in implementing projects with big data. This proactive approach helps to quickly identify risks and take measures to [...] Read more.

Symmetric data play an effective role in the risk assessment process, and, therefore, integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is essential in implementing projects with big data. This proactive approach helps to quickly identify risks and take measures to address them. However, this task is always time-consuming and costly. On the other hand, there is an essential need for an expert in this field to carry out this process manually. Therefore, in the present study, the authors propose a new methodology to automatically manage this task through a deep-learning technique. Moreover, due to the different nature of the risk data, it is not possible to consider a single neural network architecture for all of them. To overcome this problem, a Genetic Algorithm (GA) was employed to find the best architecture and hyperparameters. Finally, the risks were processed and predicted using the new proposed methodology without sending data to other servers, i.e., external servers. The results of the analysis for the first risk, i.e., latency and real-time processing, showed that using the proposed methodology can improve the detection accuracy of the failure mode by 71.52%, 54.72%, 72.47%, and 75.73% compared to the unique algorithm with the activation function of Relu and number of neurons 32, respectively, related to the one, two, three, and four hidden layers. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

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22 pages, 5750 KiB

Open AccessArticle

Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids

by Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Horia Gabriel Rusu and Ionut Anghel

Appl. Sci. 2024, 14(4), 1421; https://doi.org/10.3390/app14041421 - 08 Feb 2024

Viewed by 547

Abstract

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to [...] Read more.

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demands and the timing of their usage. In our view, grid management should leverage on EV scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging deep Q-learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising, showing the effectiveness of the proposed solution in scheduling the charging and discharging actions for a fleet of 30 EVs to align with the target energy profile in demand response programs, achieving a Pearson coefficient of 0.99. This solution also demonstrates a high degree of adaptability in effectively managing scheduling situations for EVs that involve dynamicity, influenced by various state-of-charge distributions and e-mobility features. Adaptability is achieved solely through learning from data without requiring prior knowledge, configurations, or fine-tuning. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

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17 pages, 16054 KiB

Open AccessArticle

Simulation of Spinal Cord Reflexes

by Mihai Popescu and Cristian Ravariu

Appl. Sci. 2024, 14(1), 310; https://doi.org/10.3390/app14010310 - 29 Dec 2023

Viewed by 521

Abstract

The importance of spinal reflexes is connected to the rehabilitation processes in neural prostheses and to the neuromuscular junction. In order to model neuron networks as electronic circuits, a simulation environment like LTSpice XVII or PSpice can be used to create a complete [...] Read more.

The importance of spinal reflexes is connected to the rehabilitation processes in neural prostheses and to the neuromuscular junction. In order to model neuron networks as electronic circuits, a simulation environment like LTSpice XVII or PSpice can be used to create a complete electronic description. There are four types of neurons employed in spinal reflexes: α-motoneurons, sensitive neurons, excitatory interneurons, and inhibitory interneurons. Many proposals have been made regarding methods that can be used for assimilating neurons using electronic circuits. In this paper, only a single internal model of a neuron is considered enough to simulate all four types of neurons implicated in the control loops. The main contribution of this paper is to propose the modeling of neurons using some electronic circuits designed either with a bipolar transistor or with CMOS transistors for the input and output of circuits stages. In this way, it is possible to mimic the neural pulses’ circulation along the loops of the spinal reflexes and to prove the accuracy of the simulation results with respect to the biological signals collected from the bibliographic materials. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

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22 pages, 5601 KiB

Open AccessArticle

Dynamic Depth Learning in Stacked AutoEncoders

by Sarah Alfayez, Ouiem Bchir and Mohamed Maher Ben Ismail

Appl. Sci. 2023, 13(19), 10994; https://doi.org/10.3390/app131910994 - 05 Oct 2023

Viewed by 780

Abstract

The effectiveness of deep learning models depends on their architecture and topology. Thus, it is essential to determine the optimal depth of the network. In this paper, we propose a novel approach to learn the optimal depth of a stacked AutoEncoder, called Dynamic [...] Read more.

The effectiveness of deep learning models depends on their architecture and topology. Thus, it is essential to determine the optimal depth of the network. In this paper, we propose a novel approach to learn the optimal depth of a stacked AutoEncoder, called Dynamic Depth for Stacked AutoEncoders (DDSAE). DDSAE learns in an unsupervised manner the depth of a stacked AutoEncoder while training the network model. Specifically, we propose a novel objective function, aside from the AutoEncoder’s loss function to optimize the network depth: The optimization of the objective function determines the layers’ relevance weights. Additionally, we propose an algorithm that iteratively prunes the irrelevant layers based on the learned relevance weights. The performance of DDSAE was assessed using benchmark and real datasets. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

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14 pages, 1831 KiB

Open AccessArticle

Patch-Level Consistency Regularization in Self-Supervised Transfer Learning for Fine-Grained Image Recognition

by Yejin Lee, Suho Lee and Sangheum Hwang

Appl. Sci. 2023, 13(18), 10493; https://doi.org/10.3390/app131810493 - 20 Sep 2023

Viewed by 722

Abstract

Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches [...] Read more.

Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches are based on supervised learning, which requires a large-scale labeled dataset. However, such large-scale annotated datasets for fine-grained image recognition are difficult to collect because they generally require domain expertise during the labeling process. In this study, we propose a self-supervised transfer learning method based on Vision Transformer (ViT) to learn finer representations without human annotations. Interestingly, it is observed that existing self-supervised learning methods using ViT (e.g., DINO) show poor patch-level semantic consistency, which may be detrimental to learning finer representations. Motivated by this observation, we propose a consistency loss function that encourages patch embeddings of the overlapping area between two augmented views to be similar to each other during self-supervised learning on fine-grained datasets. In addition, we explore effective transfer learning strategies to fully leverage existing self-supervised models trained on large-scale labeled datasets. Contrary to the previous literature, our findings indicate that training only the last block of ViT is effective for self-supervised transfer learning. We demonstrate the effectiveness of our proposed approach through extensive experiments using six fine-grained image classification benchmark datasets, including FGVC Aircraft, CUB-200-2011, Food-101, Oxford 102 Flowers, Stanford Cars, and Stanford Dogs. Under the linear evaluation protocol, our method achieves an average accuracy of

78.5 %

, outperforming the existing transfer learning method, which yields

77.2 %

. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

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18 pages, 6770 KiB

Open AccessArticle

Research on Improved GRU-Based Stock Price Prediction Method

by Chi Chen, Lei Xue and Wanqi Xing

Appl. Sci. 2023, 13(15), 8813; https://doi.org/10.3390/app13158813 - 30 Jul 2023

Cited by 1 | Viewed by 2557

Abstract

The prediction of stock prices holds significant implications for researchers and investors evaluating stock value and risk. In recent years, researchers have increasingly replaced traditional machine learning methods with deep learning approaches in this domain. However, the application of deep learning in forecasting [...] Read more.

The prediction of stock prices holds significant implications for researchers and investors evaluating stock value and risk. In recent years, researchers have increasingly replaced traditional machine learning methods with deep learning approaches in this domain. However, the application of deep learning in forecasting stock prices is confronted with the challenge of overfitting. To address the issue of overfitting and enhance predictive accuracy, this study proposes a stock prediction model based on a gated recurrent unit (GRU) with reconstructed datasets. This model integrates data from other stocks within the same industry, thereby enriching the extracted features and mitigating the risk of overfitting. Additionally, an auxiliary module is employed to augment the volume of data through dataset reconstruction, thereby enhancing the model’s training comprehensiveness and generalization capabilities. Experimental results demonstrate a substantial improvement in prediction accuracy across various industries. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

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19 pages, 3494 KiB

Open AccessArticle

A Multi-Layer Feature Fusion Model Based on Convolution and Attention Mechanisms for Text Classification

by Hua Yang, Shuxiang Zhang, Hao Shen, Gexiang Zhang, Xingquan Deng, Jianglin Xiong, Li Feng, Junxiong Wang, Haifeng Zhang and Shenyang Sheng

Appl. Sci. 2023, 13(14), 8550; https://doi.org/10.3390/app13148550 - 24 Jul 2023

Cited by 1 | Viewed by 1595

Abstract

Text classification is one of the fundamental tasks in natural language processing and is widely applied in various domains. CNN effectively utilizes local features, while the Attention mechanism performs well in capturing content-based global interactions. In this paper, we propose a multi-layer feature [...] Read more.

Text classification is one of the fundamental tasks in natural language processing and is widely applied in various domains. CNN effectively utilizes local features, while the Attention mechanism performs well in capturing content-based global interactions. In this paper, we propose a multi-layer feature fusion text classification model called CAC, based on the Combination of CNN and Attention. The model adopts the idea of first extracting local features and then calculating global attention, while drawing inspiration from the interaction process between membranes in membrane computing to improve the performance of text classification. Specifically, the CAC model utilizes the local feature extraction capability of CNN to transform the original semantics into a multi-dimensional feature space. Then, global attention is computed in each respective feature space to capture global contextual information within the text. Finally, the locally extracted features and globally extracted features are fused for classification. Experimental results on various public datasets demonstrate that the CAC model, which combines CNN and Attention, outperforms models that solely rely on the Attention mechanism. In terms of accuracy and performance, the CAC model also exhibits significant improvements over other models based on CNN, RNN, and Attention. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

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17 pages, 8822 KiB

Open AccessArticle

Removing Rain Streaks from Visual Image Using a Combination of Bilateral Filter and Generative Adversarial Network

by Yue Yang, Minglong Xu, Chuang Chen and Fan Xue

Appl. Sci. 2023, 13(11), 6387; https://doi.org/10.3390/app13116387 - 23 May 2023

Viewed by 1006

Abstract

Images acquired using vision sensors are easily affected by environmental limitations, especially rain streaks. These streaks will seriously reduce image quality, which, in turn, reduces the accuracy of the algorithms that use the resulting images in vision sensor systems. In this paper, we [...] Read more.

Images acquired using vision sensors are easily affected by environmental limitations, especially rain streaks. These streaks will seriously reduce image quality, which, in turn, reduces the accuracy of the algorithms that use the resulting images in vision sensor systems. In this paper, we proposed a method that combined the bilateral filter with the generative adversarial network to eliminate the interference of rain streaks. Unlike other methods that use all the information in an image as the input to the generative adversarial network, we used a bilateral filter to preprocess and separate the high frequency part of the original image. The generator for the high-frequency layer of the image was designed to generate an image with no rain streaks. The high-frequency information of the image was used in a high-frequency global discriminator designed to measure the authenticity of the generated image from multiple perspectives. We also designed a loss function based on the structural similarity index to further improve the effect of removal of the rain streaks. An ablation experiment proved the validity of the method. We also compared images in synthetic and real-world datasets. Our method could retain more image information, and the generated image was clearer. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

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