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1
Peyghambari, Sima, Yun Zhang, Hassan Heidarian, and Milad Sekandari. "One-Dimensional-Mixed Convolution Neural Network and Covariance Pooling Model for Mineral Mapping of Porphyry Copper Deposit Using PRISMA Hyperspectral Data." Photogrammetric Engineering & Remote Sensing 90, no. 8 (2024): 511–22. http://dx.doi.org/10.14358/pers.24-00006r2.
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Mapping distribution of alterations around porphyry copper deposits (PCDs) greatly affects mineral exploration. Diverse geological processes generate irregular alteration patterns with diverse spectral characteristics in mineral deposits. Applying remotely sensed hyperspectral images (HSIs) is an appealing technology for geologic surveyors to generate alteration maps. Conventional methods mainly use shallow spectral absorption features to discriminate minerals and cannot extract their important spectral information. Deep neural networks with nonlinear layers can evoke the deep spectral and spatial information of HSIs. Deep learning???based methods include fully connected neural networks, convolutional neural networks, and hybrid convolutional networks like mixed convolution neural network and covariance pooling (MCNN‐CP) algorithms. However, each has its advantages and limitations. To significantly avoid losing important spectral features, we proposed a new method by fusing a one‐dimensional convolutional neural network (1D‐CNN) with MCNN‐CP (1D‐MCNN‐CP), achieving an overall accuracy (97.44%) of mineral mapping from PRISMA HSIs. This research deduced that 1D‐MCNN‐CP improved performance and reduced misclassification errors among minerals sharing similar spectral features.
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Pavitha N, Et al. "Adaptive One-Dimensional Convolutional Neural Network for Tabular Data." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2231–35. http://dx.doi.org/10.17762/ijritcc.v11i9.9228.
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This study introduces an innovative approach for tackling the credit risk prediction problem using an Adaptive One-Dimensional Convolutional Neural Network (1D CNN). The proposed methodology is designed for one-dimensional data, such as tabular data, through a combination of feed-forward and back-propagation phases. During the feed-forward phase, neuron outputs are computed by applying convolution operations to previous layer outputs, along with bias terms and activation functions. The subsequent back-propagation phase updates weights and biases to minimize prediction errors. A custom weight initialization algorithm tailored to Leaky ReLU activation is employed to enhance model adaptability. The core of the proposed algorithm lies in its ability to process each training data sample across layers, optimizing weights and biases to achieve accurate predictions. Comprehensive evaluations are conducted on various machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, ensemble methods, and neural networks. The proposed Adaptive 1D CNN emerges as the top performer, consistently surpassing other methods in precision, recall, F1-score, and accuracy. This success is attributed to its specialized weight initialization, effective back-propagation, and integration of 1D convolutional layers.
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Bai, Maoyang, Peihao Peng, Shiqi Zhang, et al. "Mountain Forest Type Classification Based on One-Dimensional Convolutional Neural Network." Forests 14, no. 9 (2023): 1823. http://dx.doi.org/10.3390/f14091823.
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Convolutional neural networks (CNNs) have demonstrated their efficacy in remote sensing applications for mountain forest classification. However, two-dimensional convolutional neural networks (2D CNNs) require a significant manual involvement in the visual interpretation to obtain continuous polygon label data. To reduce the errors associated with manual visual interpretation and enhance classification efficiency, it is imperative to explore alternative approaches. In this research, we introduce a novel one-dimensional convolutional neural network (1D CNN) methodology that directly leverages field investigation data as labels for classifying mountain forest types based on multiple remote sensing data sources. The hyperparameters were optimised using an orthogonal table, and the model’s performance was evaluated on Mount Emei of Sichuan Province. Comparative assessments with traditional classification methods, namely, a random forest (RF) and a support vector machine (SVM), revealed superior results obtained by the proposed 1D CNN. Forest type classification using the 1D CNN achieved an impressive overall accuracy (OA) of 97.41% and a kappa coefficient (Kappa) of 0.9673, outperforming the U-Net (OA: 94.45%, Kappa: 0.9239), RF (OA: 88.99%, Kappa: 0.8488), and SVM (OA: 88.79%, Kappa: 0.8476). Moreover, the 1D CNN model was retrained using limited field investigation data from Mount Wawu in Sichuan Province and successfully classified forest types in that region, thereby demonstrating its spatial-scale transferability with an OA of 90.86% and a Kappa of 0.8879. These findings underscore the effectiveness of the proposed 1D CNN in utilising multiple remote sensing data sources for accurate mountain forest type classification. In summary, the introduced 1D CNN presents a novel, efficient, and reliable method for mountain forest type classification, offering substantial contributions to the field.
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Li, Xingwang, Xiaofei Fan, Lili Zhao, Sheng Huang, Yi He, and Xuesong Suo. "Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning." Applied Engineering in Agriculture 36, no. 5 (2020): 743–49. http://dx.doi.org/10.13031/aea.13794.
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HighlightsThis study revealed the feasibility of to classify pepper seed varieties using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN).Convolutional neural networks were adopted to develop models for prediction of seed varieties, and the performance was compared with KNN and SVM.In this experiment, the classification effect of the SVM classification model is the best, but the 1D-CNN classification model is relatively easy to implement.Abstract. When non-seed materials are mixed in seeds or seed varieties of low value are mixed in high value varieties, it will cause losses to growers or businesses. Thus, the successful discrimination of seed varieties is critical for improvement of seed ralue. In recent years, convolutional neural networks (CNNs) have been used in classification of seed varieties. The feasibility of using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN) to classify pepper seed varieties was studied. The total number of three varieties of samples was 1472, and the average spectral curve between 365nm and 970nm of the three varieties was studied. The data were analyzed using full bands of the spectrum or the feature bands selected by successive projection algorithm (SPA). SPA extracted 9 feature bands from 19 bands (430, 450, 470, 490, 515, 570, 660, 780, and 880 nm). The classification accuracy of the three classification models developed with full band using K nearest neighbors (KNN), support vector machine (SVM), and 1D-CNN were 85.81%, 97.70%, and 90.50%, respectively. With full bands, SVM and 1D-CNN performed significantly better than KNN, and SVM performed slightly better than 1D-CNN. With feature bands, the testing accuracies of SVM and 1D-CNN were 97.30% and 92.6%, respectively. Although the classification accuracy of 1D-CNN was not the highest, the ease of operation made it the most feasible method for pepper seed variety prediction. Keywords: Multispectral imaging, One-dimensional convolutional neural network, Pepper seed, Variety classification.
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Eren, Levent. "Bearing Fault Detection by One-Dimensional Convolutional Neural Networks." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8617315.
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Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.
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Zhao, Liang, Yu Bao, Yu Zhang, Ruidong Ye, and Aijuan Zhang. "Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks." Sensors 21, no. 3 (2021): 846. http://dx.doi.org/10.3390/s21030846.
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When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices.
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Liu, Bingxin, Ying Li, Guannan Li, and Anling Liu. "A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 160. http://dx.doi.org/10.3390/ijgi8040160.
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Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.
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Zhao, Jiwei, Zeyu Zhang, Peiwen Xing, and Jiahui Wu. "Network Intrusion Detection System Based on One-Dimensional Convolutional Neural Networks." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 154–60. http://dx.doi.org/10.54097/hset.v23i.3217.
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Network Intrusion leaks the personal information of network users on a large scale, causing serious security risks. It is of great significance to the Intrusion Detection Systems (IDS) to find abnormal traffic from a huge database in time. Traditional machine learning methods to detect abnormal network traffic usually need to manually extract features from the dataset, which is time-consuming and has low accuracy. This paper proposes a deep learning-based abnormal traffic detection method based on an Improved One-Dimensional Convolutional Neural Networks (ICNN-1D) to detect abnormal network traffic, which greatly improves the extraction accuracy of abnormal traffic features and improves the identification of attack traffic. CNN applies multiple filters (convolution kernels) to the raw pixel data of an image to extract and learn higher-level features. After multiple convolutions, the characteristic graph with the same number of categories as the number of samples is obtained. The experimental results on the dataset CIC-IDS2017 show that the accuracy of the hybrid algorithm is 99.8%. Compared with other learning algorithms, the accuracy of our method greatly improves, and the operation time has been reduced.
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Nourmohammadi, Farzaneh, Chetan Parmar, Elmar Wings, and Jaume Comellas. "Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks." Applied Sciences 14, no. 5 (2024): 2003. http://dx.doi.org/10.3390/app14052003.
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This paper presents a study on connection-blocking prediction in Elastic Optical Networks (EONs) using Convolutional Neural Networks (CNNs). In EONs, connections are established and torn down dynamically to fulfill the instantaneous requirements of the users. The dynamic allocation of the connections may cause spectrum fragmentation and lead to network performance degradation as connection blocking increases. Predicting potential blocking situations can be helpful during EON operations. For example, this prediction could be used in real networks to trigger proper spectrum defragmentation mechanisms at suitable moments, thereby enhancing network performance. Extensive simulations over the well-known NSFNET (National Science Foundation Network) backbone network topology were run by generating realistic traffic patterns. The obtained results are later used to train the developed machine learning models, which allow the prediction of connection-blocking events. Resource use was continuously monitored and recorded during the process. Two different Convolutional Neural Network models, a 1D CNN (One-Dimensional Convolutional Neural Network) and 2D CNN (Two-Dimensional Convolutional Neural Network), are proposed as the predicting methods, and their behavior is compared to other conventional models based on an SVM (Support Vector Machine) and KNN (K Nearest Neighbors). The results obtained show that the proposed 2D CNN predicts blocking with the best accuracy (92.17%), followed by the SVM, the proposed 1D CNN, and KNN. Results suggest that 2D CNN can be helpful in blocking prediction and might contribute to increasing the efficiency of future EON networks.
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Gao, Wenqiang, Zhiyun Xiao, and Tengfei Bao. "Detection and Identification of Potato-Typical Diseases Based on Multidimensional Fusion Atrous-CNN and Hyperspectral Data." Applied Sciences 13, no. 8 (2023): 5023. http://dx.doi.org/10.3390/app13085023.
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As one of the world’s most crucial crops, the potato is an essential source of nutrition for human activities. However, several diseases pose a severe threat to the yield and quality of potatoes. Timely and accurate detection and identification of potato diseases are of great importance. Hyperspectral imaging has emerged as an essential tool that provides rich spectral and spatial distribution information and has been widely used in potato disease detection and identification. Nevertheless, the accuracy of prediction is often low when processing hyperspectral data using a one-dimensional convolutional neural network (1D-CNN). Additionally, conventional three-dimensional convolutional neural networks (3D-CNN) often require high hardware consumption while processing hyperspectral data. In this paper, we propose an Atrous-CNN network structure that fuses multiple dimensions to address these problems. The proposed structure combines the spectral information extracted by 1D-CNN, the spatial information extracted by 2D-CNN, and the spatial spectrum information extracted by 3D-CNN. To enhance the perceptual field of the convolution kernel and reduce the loss of hyperspectral data, null convolution is utilized in 1D-CNN and 2D-CNN to extract data features. We tested the proposed structure on three real-world potato diseases and achieved recognition accuracy of up to 0.9987. The algorithm presented in this paper effectively extracts hyperspectral data feature information using three different dimensional CNNs, leading to higher recognition accuracy and reduced hardware consumption. Therefore, it is feasible to use the 1D-CNN network and hyperspectral image technology for potato plant disease identification.
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Wang, Lin, and Zuqiang Meng. "Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis." Sensors 22, no. 3 (2022): 714. http://dx.doi.org/10.3390/s22030714.
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In Chinese sentiment analysis tasks, many existing methods tend to use recurrent neural networks (e.g., long short-term memory networks and gated recurrent units) and standard one-dimensional convolutional neural networks (1D-CNN) to extract features. This is because a recurrent neural network can deal with the order dependence of the data to a certain extent and the one-dimensional convolution can extract local features. Although these methods have good performance in sentiment analysis tasks, recurrent neural networks (RNNs) cannot be parallelized, resulting in time-inefficiency, and the standard 1D-CNN can only extract a single sample feature, with the result that the feature information cannot be fully utilized. To this end, in this paper, we propose a multichannel two-dimensional convolutional neural network based on interactive features and group strategy (MCNN-IFGS) for Chinese sentiment analysis. Firstly, we no longer use word encoding technology but use character-based integer encoding to retain more fine-grained information. Besides, in character-level vectors, the interactive features of different elements are introduced to improve the dimensionality of feature vectors and supplement semantic information so that the input matches the model network. In order to ensure that more sentiment features are learned, group strategies are used to form several feature mapping groups, so the learning object is converted from the traditional single sample to the learning of the feature mapping group, so as to achieve the purpose of learning more features. Finally, multichannel two-dimensional convolutional neural networks with different sizes of convolution kernels are used to extract sentiment features of different scales. The experimental results on the Chinese dataset show that our proposed method outperforms other baseline and state-of-the-art methods.
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Lin, Wei-Cheng, and Yi-Ren Yeh. "Efficient Malware Classification by Binary Sequences with One-Dimensional Convolutional Neural Networks." Mathematics 10, no. 4 (2022): 608. http://dx.doi.org/10.3390/math10040608.
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The rapid increase of malware attacks has become one of the main threats to computer security. Finding the best way to detect malware has become a critical task in cybersecurity. Previous work shows that machine learning approaches could be a solution to address this problem. Many proposed methods convert malware executables into grayscale images and apply convolutional neural networks (CNNs) for malware classification. However, converting malware executables into images could twist the one-dimensional structure of binary codes. To address this problem, we explore the bit and byte-level sequences from malware executables and propose efficient one-dimensional (1D) CNNs for the malware classification. Our experiments evaluate our proposed 1D CNN models with two benchmark datasets. Our proposed 1D CNN models achieve better performance from the experimental results than the existing 2D CNNs malware classification models by providing smaller resizing bit/byte-level sequences with less computational cost.
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Iglesias Álvarez, Santiago, Enrique Díez Alonso, María Luisa Sánchez Rodríguez, Javier Rodríguez Rodríguez, Fernando Sánchez Lasheras, and Francisco Javier de Cos Juez. "One-Dimensional Convolutional Neural Networks for Detecting Transiting Exoplanets." Axioms 12, no. 4 (2023): 348. http://dx.doi.org/10.3390/axioms12040348.
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The transit method is one of the most relevant exoplanet detection techniques, which consists of detecting periodic eclipses in the light curves of stars. This is not always easy due to the presence of noise in the light curves, which is induced, for example, by the response of a telescope to stellar flux. For this reason, we aimed to develop an artificial neural network model that is able to detect these transits in light curves obtained from different telescopes and surveys. We created artificial light curves with and without transits to try to mimic those expected for the extended mission of the Kepler telescope (K2) in order to train and validate a 1D convolutional neural network model, which was later tested, obtaining an accuracy of 99.02% and an estimated error (loss function) of 0.03. These results, among others, helped to confirm that the 1D CNN is a good choice for working with non-phased-folded Mandel and Agol light curves with transits. It also reduces the number of light curves that have to be visually inspected to decide if they present transit-like signals and decreases the time needed for analyzing each (with respect to traditional analysis).
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Wang, Wenji. "Tool Wear State Recognition Based on 1D-CNN." Journal of Big Data and Computing 1, no. 2 (2023): 29–32. http://dx.doi.org/10.62517/jbdc.202301206.
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Machine learning classification models have the problems of complex feature engineering and unsatisfactory state recognition. In this paper, a deep learning network, One-dimensional convolutional neural networks (1D-CNN), is proposed to recognize the state of tool wear. After the original data is cleaned and pre-processed, it is directly put into the 1D-CNN model for feature self-extraction and state recognition, which improves the automation, accuracy and efficiency of the whole recognition process.
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Hooshmand, Mohammad Kazim, and Manjaiah Doddaghatta Huchaiah. "Network Intrusion Detection with 1D Convolutional Neural Networks." Digital Technologies Research and Applications 1, no. 2 (2022): 25. http://dx.doi.org/10.54963/dtra.v1i2.64.
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Computer network assets expose to various cyber threats in today’s digital era. Network Anomaly Detection Systems (NADS) play a vital role in protecting digital assets in the purview of network security. Intrusion detection systems data are imbalanced and high dimensioned, affecting models’ performance in classifying malicious traffic. This paper uses a denoising autoencoder (DAE) for feature selection to reduce data dimension. To balance the data, the authors use a combined approach of oversampling technique, adaptive synthetic (ADASYN) and a cluster-based under-sampling method using a clustering algorithm, Kmeans. Then, a one-dimensional convolutional neural network (1D-CNN) is used to perform classification. The performance of the proposed model is evaluated on UNSW-NB15 and NSL-KDD datasets. The experimental results show that the model produces a detection rate of 98.79% and 97.23% on UNSW-NB15 for binary classification and multiclass classification, respectively. In the evaluation using NSL-KDD, the model yields a detection rate of 98.52% for binary type classification and 98.16% for multiclass type classification.
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Pourdarbani, Razieh, Sajad Sabzi, Mohammad H. Rohban, et al. "One-Dimensional Convolutional Neural Networks for Hyperspectral Analysis of Nitrogen in Plant Leaves." Applied Sciences 11, no. 24 (2021): 11853. http://dx.doi.org/10.3390/app112411853.
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Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one-dimensional convolutional neural networks (1D-CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N30% (excess application of nitrogen fertilizer by 30%), N60% (60% overdose), and N90% (90% overdose). Hyperspectral data of the samples in the 400–1100 nm range were captured using a hyperspectral camera. The actual amount of nitrogen for each leaf was measured using the Kjeldahl method. Since there were statistically significant differences between the classes, an individual prediction model was designed for each class based on the 1D-CNN algorithm. The main innovation of the present research resides in the application of separate prediction models for each class, and the design of the proposed 1D-CNN regression model. The results showed that the coefficient of determination and the mean squared error for the classes N30%, N60% and N90% were 0.962, 0.0005; 0.968, 0.0003; and 0.967, 0.0007, respectively. Therefore, the proposed method can be effectively used to detect over-application of nitrogen fertilizers in plants.
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Cheng, Hu, Sophia Vinci-Booher, Jian Wang, et al. "Denoising diffusion weighted imaging data using convolutional neural networks." PLOS ONE 17, no. 9 (2022): e0274396. http://dx.doi.org/10.1371/journal.pone.0274396.
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Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.
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He, Jiajun, Ping Wu, Yizhi Tong, Xujie Zhang, Meizhen Lei, and Jinfeng Gao. "Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN." Sensors 21, no. 21 (2021): 7319. http://dx.doi.org/10.3390/s21217319.
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Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.
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Khan Mamun, Mohammad Mahbubur Rahman, and Tarek Elfouly. "Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network." Bioengineering 10, no. 7 (2023): 796. http://dx.doi.org/10.3390/bioengineering10070796.
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Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.
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Haghbin, Masoud, Juan Chiachío, Sergio Muñoz, et al. "Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements." Sensors 24, no. 14 (2024): 4627. http://dx.doi.org/10.3390/s24144627.
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This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model’s performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails’ corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways.
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Li, Heping, Jing Lu, Guixiang Tian, Huijin Yang, Jianhui Zhao, and Ning Li. "Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data." Remote Sensing 14, no. 16 (2022): 3889. http://dx.doi.org/10.3390/rs14163889.
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Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.
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Li, Dengshan, and Lina Li. "Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network." Sensors 22, no. 15 (2022): 5809. http://dx.doi.org/10.3390/s22155809.
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pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction (Rp2) was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the Rp2 was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy.
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Han, Yudong, Zhaobo Li, and Jiaqi Li. "Pavement condition detection using acceleration data collected by smartphones based on 1D convolutional neural network." Journal of the Croatian Association of Civil Engineers 76, no. 11 (2024): 979–91. https://doi.org/10.14256/jce.3958.2024.
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Vibration-based pavement condition detection methods have advanced in recent years, and it has been proven to be feasible to identify pavement conditions by analysing acceleration data. In this study, a public participation solution is proposed, and a one-dimensional convolutional neural network (1D-CNN) is introduced to directly process acceleration signals, addressing the limitations of traditional machine-learning classification methods. In this study, a smartphone and bicycle were used as the experimental tools, and 422 samples of acceleration data across the X-, Y-, and Z-axes were collected, including four types of pavement conditions: bumpy pavement, speed bumps, smooth pavement, and potholes. Five types of 1D-CNN with different activation functions and network structures were designed to classify the data and were compared with machine learning algorithms, including support vector machine (SVM) and radial basis function (RBF) neural networks. The results show that a 1D-CNN, with three convolution layers and three pooling layers using the ReLU activation function, achieved the best classification performance, with a classification accuracy of 0.9976. Compared with SVM and RBF neural networks, CNN not only saves considerable time by eliminating manual feature extraction operations but also provides higher classification accuracy.
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Rala Cordeiro, João, António Raimundo, Octavian Postolache, and Pedro Sebastião. "Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements." Sensors 21, no. 23 (2021): 7990. http://dx.doi.org/10.3390/s21237990.
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In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript’s research question (a real-life clinical case) were provided.
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Nguyen, Thi Hoai Thu, Năng Văn Phạm, and Quốc Hưng Hoàng. "Bearing fault diagnosis by machine learning and deep learning-based models: A comparative study applying for HUST bearing dataset." Journal of Military Science and Technology 103 (May 26, 2025): 31–39. https://doi.org/10.54939/1859-1043.j.mst.103.2025.31-39.
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Diagnosing bearing faults is essential for ensuring the reliability and operational safety of mechanical and electronic systems. This paper presents a comparative analysis of different machine learning-based models for classifying bearing fault conditions, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, One-Dimensional Convolutional Neural Networks (1D-CNN), Two-Dimensional Convolutional Neural Networks (2D-CNN), and Transformer model. These models are applied to the HUST bearing dataset and evaluated based on their ability to accurately classify defects from vibration signal data. The results indicate that 1D-CNN, 2D-CNN, and Transformer model exhibit superior performance in bearing fault diagnosis. 1D-CNN attained 99.8% accuracy on the training set and 99.83% on the test set, followed by 2D-CNN with 99.1% and 99.3%, respectively. The Transformer model also performed well, reaching 99.7% accuracy within 1 hour of training, similar to 1D-CNN (1 hour) and 2D-CNN (0.8 hours). In contrast, LSTM and SVM exhibited lower accuracy and significantly longer training times, with LSTM requiring 11.5 hours and SVM 8 hours. These findings suggest that 1D-CNN, 2D-CNN, and the Transformer model are highly effective approaches for bearing fault diagnosis, with the Transformer model achieving performance and training efficiency comparable to CNN-based models.
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Deng, Guohao, Dang Wang, and Weixin Gao. "Active and Reactive Power Coordination Optimization of the Active Distribution Network." Journal of Physics: Conference Series 2450, no. 1 (2023): 012023. http://dx.doi.org/10.1088/1742-6596/2450/1/012023.
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Abstract The operation and control of the active distribution network are faced with great challenges due to a mass of tunable and controllable devices connected to the network, resulting in large active power loss and voltage deviation. In this paper, a method of active and reactive power coordination optimization for the active distribution network based on a one-dimensional convolutional neural network (1D-CNN) is proposed. This method can mine valuable information from the historical data of distribution networks, and use one-dimensional convolutional neural networks to map the complex nonlinear relationship between node load and optimization strategy. The simulation results of the modified IEEE33 node distribution network system show that the active power loss and the node voltage deviation of the proposed method are significantly reduced.
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Du, Canyi, Rui Zhong, Yishen Zhuo, et al. "Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method." Engineering Research Express 4, no. 1 (2022): 015003. http://dx.doi.org/10.1088/2631-8695/ac4834.
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Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.
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Guessoum, Sonia, Santiago Belda, Jose M. Ferrandiz, et al. "The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)." Sensors 22, no. 23 (2022): 9517. http://dx.doi.org/10.3390/s22239517.
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Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.
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Chen, Chih-Cheng, Zhen Liu, Guangsong Yang, Chia-Chun Wu, and Qiubo Ye. "An Improved Fault Diagnosis Using 1D-Convolutional Neural Network Model." Electronics 10, no. 1 (2020): 59. http://dx.doi.org/10.3390/electronics10010059.
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The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction in order to enhance the accuracy. The one-dimensional convolution neural network (1D-CNN) method can not only diagnose bearing faults accurately, but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. In this paper, the 1D-CNN network architecture is proposed in order to effectively improve the accuracy of the diagnosis of rolling bearing, and the number of convolution kernels decreases with the reduction of the convolution kernel size. The method obtains high accuracy and improves the generalizing ability by introducing the dropout operation. The experimental results show 99.2% of the average accuracy under a single load and 98.83% under different loads.
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Hwang, Sung-Wook, Geungyong Park, Jinho Kim, Kwang-Ho Kang, and Won-Hee Lee. "One-dimensional convolutional neural networks with infrared spectroscopy for classifying the origin of printing paper." BioResources 19, no. 1 (2024): 1633–51. http://dx.doi.org/10.15376/biores.19.1.1633-1651.
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Herein, the challenge of accurately classifying the manufacturing origin of printing paper, including continent, country, and specific product, was addressed. One-dimensional convolutional neural network (1D CNN) models trained on infrared (IR) spectrum data acquired from printing paper samples were used for the task. The preprocessing of the IR spectra through a second-derivative transformation and the restriction of the spectral range to 1800 to 1200 cm-1 improved the classification performance of the model. The outcomes were highly promising. Models trained on second-derivative IR spectra in the 1800 to 1200-cm-1 range exhibited perfect classification for the manufacturing continent and country, with an impressive F1 score of 0.980 for product classification. Notably, the developed 1D CNN model outperformed traditional machine learning classifiers, such as support vector machines and feed-forward neural networks. In addition, the application of data point attribution enhanced the transparency of the decision-making process of the model, offering insights into the spectral patterns that affect classification. This study makes a considerable contribution to printing paper classification, with potential implications for accurate origin identification in various fields.
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Zhang, Bo, Tao Xu, Wen Chen, and Chongyang Zhang. "Predicting the Remaining Time before Earthquake Occurrence Based on Mel Spectrogram Features Extraction and Ensemble Learning." Applied Sciences 13, no. 22 (2023): 12268. http://dx.doi.org/10.3390/app132212268.
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Predicting the remaining time before the next earthquake based on seismic signals generated in a laboratory setting is a challenging research task that is of significant importance for earthquake hazard assessment. In this study, we employed a mel spectrogram and the mel frequency cepstral coefficient (MFCC) to extract relevant features from seismic signals. Furthermore, we proposed a deep learning model with a hierarchical structure. This model combines the characteristics of long short-term memory (LSTM), one-dimensional convolutional neural networks (1D-CNN), and two-dimensional convolutional neural networks (2D-CNN). Additionally, we applied a stacking model fusion strategy, combining gradient boosting trees with deep learning models to achieve optimal performance. We compared the performance of the aforementioned feature extraction methods and related models for earthquake prediction. The results revealed a significant improvement in predictive performance when the mel spectrogram and stacking were introduced. Additionally, we found that the combination of 1D-CNN and 2D-CNN has unique advantages in handling time-series problems.
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Park, Tae-Hui, Da-Seul Jang, Gyeong-Min Bae, Kyung-Min Kim, and Johng-Hwa Ahn. "Selection of Input Variables and Comparison of Artificial Neural Networks and One-Dimensional Convolutional Neural Networks for Prediction of Wind Power Generation in Yeongheung Wind Power Plant." Journal of Korean Society of Environmental Engineers 43, no. 4 (2021): 219–29. http://dx.doi.org/10.4491/ksee.2021.43.4.219.
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Objectives : In this study, deep learning models of artificial neural network (ANN) and one-dimension convolutional neural networks (1D-CNN) were compared to predict nonlinear wind power generation at Yeongheung wind power plant.Methods : The study site was Yeongheung-do, which has a 46 MW wind power plant. Hourly wind power and meteorological data from January to December 2018 were collected. After pre-processing with standardscaler, the training data were 64%, the validation data were 16%, and the test data were 20%. The optimum input variables of the model were selected using literature, and trial and error method. Rectified linear unit was used as the activation function. Hyperparameters were adjusted by trial and error method to optimized models. To compare the optimized models, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were used as the performance efficiency. Both ANN, and 1D-CNN were imported from the Keras library, and all of the performance efficiency was imported from the Scikit-learn library.Results and Discussion : The optimized input variables in this study were wind speed, wind direction, temperature, and humidity. The optimized ANN performance was R2=0.848, MAE=1.054, and RMSE=1.616, and the hyperparameters were 8 hidden layers with 100 nodes in each layer. The optimized 1D-CNN (R2=0.875, MAE=0.982, and RMSE=1.583) had 4 convolutional layers and the number of filters were 64, 128, 64, and 32 in order from the first layer, and one hidden fully connected layer had 100 nodes. The 1D-CNN had higher R2, and lower MAE and RMSE than the ANN. Therefore, the 1D-CNN was selected as the optimized model to predict wind generation of the Yeongheung wind power plant.Conclusions : The optimized 1D-CNN model in this study was more effective in predicting the Yeongheung wind power plant than the ANN. The optimal input variables were wind speed, wind direction, temperature, and humidity.
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Radhi, Ahmed Thamer, Wael Hussein Zayer, and Adel Manaa Dakhil. "Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 3 (2021): 1928. http://dx.doi.org/10.11591/ijpeds.v12.i3.pp1928-1939.
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<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>
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Shen, Dan, and Wenjia Zhao. "A Method for Improving the Pronunciation Quality of Vocal Music Students Based on Big Data Technology." International Journal of Web-Based Learning and Teaching Technologies 19, no. 1 (2023): 1–18. http://dx.doi.org/10.4018/ijwltt.335034.
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With the development of internet technology, big data has been used to evaluate the singing and pronunciation quality of vocal students. However, current methods have several problems such as poor information fusion efficiency, low algorithm robustness, and low recognition accuracy under low signal-to-noise ratio. To address these issues, this article proposes a new method for evaluating sound quality based on one-dimensional convolutional neural networks. It uses sound preprocessing, BP neural networks, wavelet neural networks, and one-dimensional CNNs to improve pronunciation quality. The proposed 1D CNN network is more suitable for one-dimensional sound signals and can effectively solve problems such as feature information fusion, pitch period detection, and network construction. It can evaluate singing art sound quality with minimum errors, good robustness, and strong portability. This method can be used for the evaluation and diagnosis of voice diseases, helping to improve students' professional abilities.
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Yoon, Jinsung, Neungyun Kim, Donghyun Lee, Su-Jung Lee, Gil-Ho Kwak, and Tae-Hwan Kim. "A Resource-Efficient Keyword Spotting System Based on a One-Dimensional Binary Convolutional Neural Network." Electronics 12, no. 18 (2023): 3964. http://dx.doi.org/10.3390/electronics12183964.
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This paper proposes a resource-efficient keyword spotting (KWS) system based on a convolutional neural network (CNN). The end-to-end KWS process is performed based solely on 1D-CNN inference, where features are first extracted from a few convolutional blocks, and then the keywords are classified using a few fully connected blocks. The 1D-CNN model is binarized to reduce resource usage, and its inference is executed by employing a dedicated engine. This engine is designed to skip redundant operations, enabling high inference speed despite its low complexity. The proposed system is implemented using 6895 ALUTs in an Intel Cyclone V FPGA by integrating the essential components for performing the KWS process. In the system, the latency required to process a frame is 22 ms, and the spotting accuracy is 91.80% in an environment where the signal-to-noise ratio is 10 dB for Google speech commands dataset version 2.
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Chong, Xulei, Yating Gao, Ru Zhang, Jianyi Liu, Xingjie Huang, and Jinmeng Zhao. "Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion." Electronics 11, no. 19 (2022): 3064. http://dx.doi.org/10.3390/electronics11193064.
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A malware family classification method based on Efficient-Net and 1D-CNN fusion is proposed. Given the problem that some local information of malware itself as one-dimensional data will be lost when the malware is imaged, the malware is converted into an image and one-dimensional vector and then input into two neural networks. The network of two-dimensional convolution architecture is used to extract the texture features of malware, and the one-dimensional convolution is used to extract the features of local adjacent information, the deep characteristics of different networks are fused, and the two networks are modified at the same time during backpropagation. This method not only extracts the texture features of malware but also saves the features of the malware itself as one-dimensional data, which shows better performance for multiple datasets.
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Jiang, Wanlu, Chenyang Wang, Jiayun Zou, and Shuqing Zhang. "Application of Deep Learning in Fault Diagnosis of Rotating Machinery." Processes 9, no. 6 (2021): 919. http://dx.doi.org/10.3390/pr9060919.
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The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.
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Wu, Bin, Shibo Yuan, Peng Li, Zehuan Jing, Shao Huang, and Yaodong Zhao. "Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism." Sensors 20, no. 21 (2020): 6350. http://dx.doi.org/10.3390/s20216350.
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As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.
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Munyaneza, Olivier, and Jung Woo Sohn. "Multiscale 1D-CNN for Damage Severity Classification and Localization Based on Lamb Wave in Laminated Composites." Mathematics 13, no. 3 (2025): 398. https://doi.org/10.3390/math13030398.
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Lamb-wave-based structural health monitoring is widely employed to detect and localize damage in composite plates; however, interpreting Lamb wave signals remains challenging due to their dispersive characteristics. Although convolutional neural networks (CNNs) demonstrate a significant capability for pattern recognition within these signals relative to other machine learning models, CNNs frequently encounter difficulties in capturing all the underlying patterns when the damage severity varies. To address this issue, we propose a multiscale, one-dimensional convolutional neural network (MS-1D-CNN) to assess the damage severity and localize damage in laminated plates. The MS-1D-CNN is capable of learning both low- and high-level features, enabling it to distinguish between minor and severe damage. The dataset was obtained experimentally via a sparse array of four lead zirconate titanates, with signals from twelve paths fused and downsampled before being input into the model. The efficiency of the model was evaluated using accuracy, precision, recall, and F1-score metrics for severity identification, along with the mean squared error, mean absolute error, and R2 for damage localization. The experimental results indicated that the proposed MS-1D-CNN outperformed support vector machine and artificial neural network models, achieving higher accuracy in both identifying damage severity and localizing damage with minimal error.
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Qazi, Emad Ul Haq, Abdulrazaq Almorjan, and Tanveer Zia. "A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection." Applied Sciences 12, no. 16 (2022): 7986. http://dx.doi.org/10.3390/app12167986.
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The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we detect four different types of network intrusions, i.e., DoS Hulk, DDoS, and DoS Goldeneye which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 98.96% as demonstrated in the experimental results.
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Shen, Yuchi, Jing Wu, Junfeng Chen, Weiwei Zhang, Xiaolin Yang, and Hongwei Ma. "Quantitative Detection of Pipeline Cracks Based on Ultrasonic Guided Waves and Convolutional Neural Network." Sensors 24, no. 4 (2024): 1204. http://dx.doi.org/10.3390/s24041204.
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In this study, a quantitative detection method of pipeline cracks based on a one-dimensional convolutional neural network (1D-CNN) was developed using the time-domain signal of ultrasonic guided waves and the crack size of the pipeline as the input and output, respectively. Pipeline ultrasonic guided wave detection signals under different crack defect conditions were obtained via numerical simulations and experiments, and these signals were input as features into a multi-layer perceptron and one-dimensional convolutional neural network (1D-CNN) for training. The results revealed that the 1D-CNN performed better in the quantitative analysis of pipeline crack defects, with an error of less than 2% in the simulated and experimental data, and it could effectively evaluate the size of crack defects from the echo signals under different frequency excitations. Thus, by combining the ultrasonic guided wave detection technology and CNN, a quantitative analysis of pipeline crack defects can be effectively realized.
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Hindarto, Djarot. "Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis." sinkron 8, no. 4 (2023): 2537–46. http://dx.doi.org/10.33395/sinkron.v8i4.13048.
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This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets. A dataset comprising text reviews with positive or negative sentiment labels was evaluated. All evaluated models demonstrated an extremely high accuracy, ranging from 99.81% to 99.99%. Apart from that, the loss generated by these models is also low, ranging from 0.0043 to 0.0021. However, there are minor performance differences between the evaluated architectures. The Long Short-Term Memory and Gated Recurrent Unit models mainly perform marginally better than the Simple Recurrent Neural Network, albeit with slightly lower accuracy and loss. In the meantime, the Bidirectional Recurrent Neural Network model demonstrates competitive performance, as it can effectively manage text context from both directions. Additionally, One-Dimensional Convolutional Neural Networks provide satisfactory results, indicating that convolution-based approaches are also effective in sentiment analysis. The findings of this study provide practitioners with essential insights for selecting an appropriate architecture for sentiment analysis tasks. While all models yield excellent performance, the choice of architecture can impact computational efficiency and training time. Therefore, a comprehensive comprehension of the respective characteristics of Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks is essential for making more informed decisions when constructing sentiment analysis models.
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Lekkas, Georgios, Eleni Vrochidou, and George A. Papakostas. "Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks." BioMedInformatics 5, no. 1 (2025): 7. https://doi.org/10.3390/biomedinformatics5010007.
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Background: Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. Methods: We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. Results: These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. Conclusions: This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data.
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Li, Xiuyan, Rengui Lu, Qi Wang, et al. "One-dimensional convolutional neural network (1D-CNN) image reconstruction for electrical impedance tomography." Review of Scientific Instruments 91, no. 12 (2020): 124704. http://dx.doi.org/10.1063/5.0025881.
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Sarjaš, Andrej, Blaž Pongrac, and Dušan Gleich. "Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy." Sensors 21, no. 14 (2021): 4709. http://dx.doi.org/10.3390/s21144709.
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This paper presents an automatic classification of plastic material’s inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised learning approach. A THz frequency band between 0.1–1.2 THz produced a one-dimensional (1D) vector that is almost impossible to classify directly using supervised learning. This paper proposes a novel pre-processing of 1D THz data that transforms 1D data into 2D data, which are processed efficiently using a convolutional neural network. The proposed pre-processing algorithm consists of four steps: peak detection, envelope extraction, and a down-sampling procedure. The last main step introduces the windowing with spectrum dilatation that reorders 1D data into 2D data that can be considered as an image. The spectrum dilation techniques ensure the classifier’s robustness by suppressing measurement bias, reducing the complexity of the THz dataset with negligible loss of accuracy, and speeding up the network classification. The experimental results showed that the proposed approach achieved high accuracy using a CNN classifier, and outperforms 1D classification of THz data using support vector machine, naive Bayes, and other popular classification algorithms.
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Ahmed, Thamer Radhi, Hussein Zayer Wael, and Manaa Dakhil Adel. "Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 3 (2021): 1928–39. https://doi.org/10.11591/ijpeds.v12.i3.pp1928-1939.
Full textAbstract:
This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using onedimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1DCNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
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Gao, Xiangang, Bin Wu, Peng Li, and Zehuan Jing. "1D-CNN-Transformer for Radar Emitter Identification and Implemented on FPGA." Remote Sensing 16, no. 16 (2024): 2962. http://dx.doi.org/10.3390/rs16162962.
Full textAbstract:
Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to the requirements of the low power consumption and high-performance processing of SEI on embedded devices, so this article proposes solutions from the aspects of software and hardware. From the software side, we design a Transformer variant network, lightweight convolutional Transformer (LW-CT) that supports parameter sharing. Then, we cascade convolutional neural networks (CNNs) and the LW-CT to construct a one-dimensional-CNN-Transformer(1D-CNN-Transformer) lightweight neural network model that can capture the long-range dependencies of radar emitter signals and extract signal spatial domain features meanwhile. In terms of hardware, we design a low-power neural network accelerator based on an FPGA to complete the real-time recognition of radar emitter signals. The accelerator not only designs high-efficiency computing engines for the network, but also devises a reconfigurable buffer called “Ping-pong CBUF” and two-level pipeline architecture for the convolution layer for alleviating the bottleneck caused by the off-chip storage access bandwidth. Experimental results show that the algorithm can achieve a high recognition performance of SEI with a low calculation overhead. In addition, the hardware acceleration platform not only perfectly meets the requirements of the radar emitter recognition system for low power consumption and high-performance processing, but also outperforms the accelerators in other papers in terms of the energy efficiency ratio of Transformer layer processing.
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Tu, Fengmiao, and Suixian Yang. "Application of a one-dimensional convolutional neural network in defect size inversion of oil and gas pipelines." Insight - Non-Destructive Testing and Condition Monitoring 67, no. 1 (2025): 20–26. https://doi.org/10.1784/insi.2025.67.1.20.
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The strong non-linearity and low prediction accuracy of defect depth inversion often lead to challenges in magnetic flux leakage (MFL) detection and evaluation. If a one-dimensional convolutional neural network (1D-CNN) model could automatically extract the features of the original MFL signal and its simple and compact configuration, then real-time and low-cost hardware implementation should be achievable in the future. Therefore, in this work, a pipeline defect size inversion method based on a 1D-CNN model is proposed and the optimal network hierarchy is explored. In order to fully fuse the three-axis MFL signal and input it into the 1D-CNN for defect size inversion, three signal input strategies suitable for MFL defect detection based on the 1D-CNN are proposed and the quantitative results of the different three-axis MFL signal input strategies for an artificial defect dataset are compared and analysed. Experimental results indicate that using a lateral connection between the three-axis MFL signals can effectively achieve high-precision quantification of defect size and especially the quantification of defect depth. In addition, the comprehensive comparison and analysis with other network structures and traditional algorithms also verify that the 1D-CNN method still has advantages in accuracy and time complexity.
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Fadhil, Muhammad Farhan, and Amalia Zahra. "Speech emotion recognition with optimized multi-feature stack using deep convolutional neural networks." Bulletin of Electrical Engineering and Informatics 13, no. 6 (2024): 4147–56. http://dx.doi.org/10.11591/eei.v13i6.6044.
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The human emotion in communication plays a significant role that can influence how the context of the message is perceived by others. Speech emotion recognition (SER) is one of a field study that is very intriguing to explore because human-computer interaction (HCI) related technologies such as virtual assistant that are implemented nowadays rarely considered the emotion contained in the information relayed by human speech. One of the most widely used ways to perform SER is by extracting features of speech such as mel frequency cepstral coefficient (MFCC), mel-spectrogram, spectral contrast, tonnetz, and chromagram from the signal and using a one-dimensional (1D) convolutional neural network (CNN) as a classifier. This study shows the impact of implementing a combination of an optimized multi-feature stack and optimized 1D deep CNN model. The result of the model proposed in this study has an accuracy of 90.10% for classifying 8 different emotions performed on the ryerson audio-visual database of emotional speech and song (RAVDESS) dataset.
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Xu, Xiaohui, Wenjun Huo, Fei Li, and Hongbin Zhou. "Classification of Liquid Ingress in GFRP Honeycomb Based on One-Dimension Sequential Model Using THz-TDS." Sensors 23, no. 3 (2023): 1149. http://dx.doi.org/10.3390/s23031149.
Full textAbstract:
Honeycomb structure composites are taking an increasing proportion in aircraft manufacturing because of their high strength-to-weight ratio, good fatigue resistance, and low manufacturing cost. However, the hollow structure is very prone to liquid ingress. Here, we report a fast and automatic classification approach for water, alcohol, and oil filled in glass fiber reinforced polymer (GFRP) honeycomb structures through terahertz time-domain spectroscopy (THz-TDS). We propose an improved one-dimensional convolutional neural network (1D-CNN) model, and compared it with long short-term memory (LSTM) and ordinary 1D-CNN models, which are classification networks based on one dimension sequenced signals. The automated liquid classification results show that the LSTM model has the best performance for the time-domain signals, while the improved 1D-CNN model performed best for the frequency-domain signals.