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Journal articles on the topic 'Dimensional Convolutional Neural Network (1D-CNN)'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

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

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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 convo

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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 i

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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 f

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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 mecha

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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), rando

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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 succes

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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 spa

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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

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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 collec

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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 sta

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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-CN

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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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Zhang, Yi, Fuzhou Liu, Jie Guan, and Yongli Zhu. "Time-frequency Fusion Method via Convolutional Neural Network for Partial Discharge Classification." Journal of Physics: Conference Series 2452, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2452/1/012014.

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Abstract To improve the accuracy of partial discharge (PD) pattern recognition by jointing time-domain (TD) and frequency-domain (FD) information, a time-frequency (TF) fusion method via convolution neural network (CNN) is proposed in this paper. Firstly, PD signals are represented by PD waveform images and transformed into the envelope of variational mode decomposition-based Hilbert marginal spectrum (VHMS). Secondly, a fusion network, FuNet involving a 2-dimensional CNN (2D-CNN), a 1D-CNN, and a multilayer perceptron (MLP), is established to join TF information. In FuNet, the 2D-CNN inputted

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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 techni

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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 (G

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Li, Menghu, Tianhong Pan, Yang Bai, and Qi Chen. "Development of a calibration model for near infrared spectroscopy using a convolutional neural network." Journal of Near Infrared Spectroscopy 30, no. 2 (2022): 89–96. http://dx.doi.org/10.1177/09670335211057234.

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Development of qualitative or quantitative models is essential to exploit the full potential of near infrared (NIR) spectroscopy. In tandem with one-dimensional convolutional neural network (1D-CNN), a data-driven model is developed using NIR spectroscopy to estimate organic contents. First, the 1D-CNN model is designed to capture the features of the NIR spectra by means of several convolutional and pooling operations. Then, the suitable hyper-parameters of 1D-CNN are obtained by using the grid search algorithm to achieve the optimal performance. Furthermore, the dropout operation is added int

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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 dire

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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

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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 cluste

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Dong, Shidu, Zhi Liu, Huaqiu Wang, Yihao Zhang, and Shaoguo Cui. "A Separate 3D Convolutional Neural Network Architecture for 3D Medical Image Semantic Segmentation." Journal of Medical Imaging and Health Informatics 9, no. 8 (2019): 1705–16. http://dx.doi.org/10.1166/jmihi.2019.2797.

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To exploit three-dimensional (3D) context information and improve 3D medical image semantic segmentation, we propose a separate 3D (S3D) convolution neural network (CNN) architecture. First, a two-dimensional (2D) CNN is used to extract the 2D features of each slice in the xy-plane of 3D medical images. Second, one-dimensional (1D) features reassembled from the 2D features in the z-axis are input into a 1D-CNN and are then classified feature-wise. Analysis shows that S3D-CNN has lower time complexity, fewer parameters and less memory space requirements than other 3D-CNNs with a similar structu

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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 tr

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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 c

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Baydemir, Recep, Fatma Latifoğlu, and Fırat Orhanbulucu. "Classification Mental Workload Levels from EEG Signals with 1D Convolutional Neural Network." European Journal of Research and Development 2, no. 4 (2022): 13–23. http://dx.doi.org/10.56038/ejrnd.v2i4.193.

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Mental workload (MWL) can be estimated according to the state of cognitive capacity after an activity. In this study, it is aimed to classify MWL levels from Electroencephalogram (EEG) signals recorded from a task moment. Using the proposed one-dimensional convolutional neural network (1D-CNN) model in the study, low (L) and high (H) level WL states were classified. The classification process was carried out in two stages. EEG signals passed through the preprocessing stage were classified with 1D-CNN in the first stage. In the second step, these signals were decomposed into subbands by applyin

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Chen, Dong, and Young Hoon Joo. "A Novel Approach to 3D-DOA Estimation of Stationary EM Signals Using Convolutional Neural Networks." Sensors 20, no. 10 (2020): 2761. http://dx.doi.org/10.3390/s20102761.

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This paper proposes a novel three-dimensional direction-of-arrival (3D-DOA) estimation method for electromagnetic (EM) signals using convolutional neural networks (CNN) in a Gaussian or non-Gaussian noise environment. First of all, in the presence of Gaussian noise, four output covariance matrices of the uniform triangular array (UTA) are normalized and then fed into four neural networks for 1D-DOA estimation with identical parameters in parallel; then four 1D-DOA estimations of the UTA can be obtained, and finally, the 3D-DOA estimation could be obtained through post-processing. Secondly, in

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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 t

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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 erro

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Huang, Chih-Yung, and Zaky Dzulfikri. "Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network." Sensors 21, no. 1 (2021): 262. http://dx.doi.org/10.3390/s21010262.

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Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stampin

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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 non

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Qin, Yufeng, and Xianjun Shi. "Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets." Applied Sciences 12, no. 17 (2022): 8474. http://dx.doi.org/10.3390/app12178474.

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As a critical component in industrial systems, timely and accurate fault diagnosis of rolling bearings is closely related to reliability and safety. Since the equipment usually operates in normal conditions with few fault samples, unbalanced data distribution problems lead to poor fault diagnosis ability. To address the above problems, a two-channel convolutional neural network (TC-CNN) model is proposed. Firstly, the frequency spectrum of the vibration signal is extracted using the Fast Fourier Transform (FFT), and the frequency spectrum is used as the input to the one-dimensional convolution

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Riese, F. M., and S. Keller. "SOIL TEXTURE CLASSIFICATION WITH 1D CONVOLUTIONAL NEURAL NETWORKS BASED ON HYPERSPECTRAL DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 615–21. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-615-2019.

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<p><strong>Abstract.</strong> Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, and the LucasCoordConv with an additional coordinates layer. Furthermore, we modify two existing 1D CNN approaches for the presented classification task. The code of all five CNN approaches is available on GitHub (Riese, 2019). We ev

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Lee, Sijin, Kwang-Sig Lee, Hyun-Joon Park, et al. "A Comparative Study of Convolutional Neural Network and Recurrent Neural Network Models for the Analysis of Cardiac Arrest Rhythms During Cardiopulmonary Resuscitation." Applied Sciences 15, no. 8 (2025): 4148. https://doi.org/10.3390/app15084148.

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To develop and evaluate deep learning models for cardiac arrest rhythm classification during cardiopulmonary resuscitation (CPR), we analyzed 508 electrocardiogram (ECG) segments (each 4 s in duration, recorded at 250 Hz) from 131 cardiac arrest patients. Compression-affected segments were recorded during chest compressions, while non-compression segments were extracted during compression pauses or immediately after return of spontaneous circulation (ROSC) declaration. One-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) models were employed for four binary

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Bozorov, Otabek Eshbo'ri o'g'li Sayyod Dostonov Sultonovich Yusupov Akmal Norxidir o'g'li. "ONE-DIMENSIONAL NEURON NETWORKS." INTERNATIONAL BULLETIN OF ENGINEERING AND TECHNOLOGY 3, no. 4 (2023): 103–10. https://doi.org/10.5281/zenodo.7824130.

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One-dimensional neural networks, also known as 1D convolutional neural networks (CNNs), are a type of neural network commonly used for processing time series and sequential data. Unlike traditional feedforward neural networks that operate on vector inputs, 1D CNNs operate on 1D sequences, such as audio signals, text, and physiological signals.

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Zhang, Dengyong, Haoting Zhou, Feng Li, Lebing Zhang, and Jianxin Wang. "A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification." Computational and Mathematical Methods in Medicine 2022 (November 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/7401175.

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Aiming at arrhythmia heartbeats classification, a novel multifeature fusion deep learning-based method is proposed. The stationary wavelet transforms (SWT) and RR interval features are firstly extracted. Based on the traditional one-dimensional convolutional neural network (1D-CNN), a parallel multibranch convolutional network is designed for training. The subband of SWT is input into the multiscale 1D-CNN separately. The output fused with RR interval features are fed to the fully connected layer for classification. To achieve the lightweight network while maintaining the powerful inference ca

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Rifai, Ahmad, Muhammad Naufal Rachmamtullah, and Winda Kurnia Sari. "ECG Signal Denoising Using 1D Convolutional Neural Network." Computer Engineering and Applications Journal 13, no. 2 (2024): 60–68. http://dx.doi.org/10.18495/comengapp.v13i2.482.

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Electrocardiogram (ECG) signals are crucial for monitoring cardiac activity and diagnosing various cardiovascular conditions. However, these signals are often contaminated by different types of noise, such as baseline wander, muscle artifacts, and power line interference, which can obscure critical information and hinder accurate diagnosis. This study used a 1-Dimensional Convolutional Neural Network (1D CNN) architecture with seven convolutional layers for denoising ECG signals. The model utilizes a fully convolutional autoencoder approach, comprising an encoder that transforms noisy input si

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Hu, Jinlong, Yuezhen Kuang, Bin Liao, Lijie Cao, Shoubin Dong, and Ping Li. "A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification." Computational Intelligence and Neuroscience 2019 (December 31, 2019): 1–9. http://dx.doi.org/10.1155/2019/5065214.

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Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely use

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Zhou, Haixia, and Jindong Chen. "An Enterprise Service Demand Classification Method Based on One-Dimensional Convolutional Neural Network with Cross-Entropy Loss and Enterprise Portrait." Entropy 25, no. 8 (2023): 1211. http://dx.doi.org/10.3390/e25081211.

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To address the diverse needs of enterprise users and the cold-start issue of recommendation system, this paper proposes a quality-service demand classification method—1D-CNN-CrossEntorpyLoss, based on cross-entropy loss and one-dimensional convolutional neural network (1D-CNN) with the comprehensive enterprise quality portrait labels. The main idea of 1D-CNN-CrossEntorpyLoss is to use cross-entropy to minimize the loss of 1D-CNN model and enhance the performance of the enterprise quality-service demand classification. The transaction data of the enterprise quality-service platform are selected

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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 v

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Li, Yuxing, Zhaoyu Gu, and Xiumei Fan. "Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network." Sensors 24, no. 5 (2024): 1680. http://dx.doi.org/10.3390/s24051680.

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This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization–slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization–slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization–slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to

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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 u

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Kim, HyunBum, Juhyeong Jeon, Yeon Jae Han, et al. "Convolutional Neural Network Classifies Pathological Voice Change in Laryngeal Cancer with High Accuracy." Journal of Clinical Medicine 9, no. 11 (2020): 3415. http://dx.doi.org/10.3390/jcm9113415.

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Voice changes may be the earliest signs in laryngeal cancer. We investigated whether automated voice signal analysis can be used to distinguish patients with laryngeal cancer from healthy subjects. We extracted features using the software package for speech analysis in phonetics (PRAAT) and calculated the Mel-frequency cepstral coefficients (MFCCs) from voice samples of a vowel sound of /a:/. The proposed method was tested with six algorithms: support vector machine (SVM), extreme gradient boosting (XGBoost), light gradient boosted machine (LGBM), artificial neural network (ANN), one-dimension

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Ullah, Amin, Sadaqat ur Rehman, Shanshan Tu, Raja Majid Mehmood, Fawad, and Muhammad Ehatisham-ul-haq. "A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal." Sensors 21, no. 3 (2021): 951. http://dx.doi.org/10.3390/s21030951.

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Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN

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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 u

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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 cont

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Lee, Min Seop, Yun Kyu Lee, Dong Sung Pae, Myo Taeg Lim, Dong Won Kim, and Tae Koo Kang. "Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network." Applied Sciences 9, no. 16 (2019): 3355. http://dx.doi.org/10.3390/app9163355.

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Physiological signals contain considerable information regarding emotions. This paper investigated the ability of photoplethysmogram (PPG) signals to recognize emotion, adopting a two-dimensional emotion model based on valence and arousal to represent human feelings. The main purpose was to recognize short term emotion using a single PPG signal pulse. We used a one-dimensional convolutional neural network (1D CNN) to extract PPG signal features to classify the valence and arousal. We split the PPG signal into a single 1.1 s pulse and normalized it for input to the neural network based on the p

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Zhang, Chuan, Min Yi, Fawang Ye, Qingjun Xu, Xinchun Li, and Qingqing Gan. "Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China." Remote Sensing 14, no. 20 (2022): 5122. http://dx.doi.org/10.3390/rs14205122.

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Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe ura

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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.

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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

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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-

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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

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Liu, Dali, Xuchen Zhao, Wenjing Cao, Wei Wang, and Yi Lu. "Design and Performance Evaluation of a Deep Neural Network for Spectrum Recognition of Underwater Targets." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/8848507.

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Due to the complexity of the underwater environment, underwater acoustic target recognition (UATR) has always been challenging. Although deep neural networks (DNN) have been used in UATR and some achievements have been made, the performance is not satisfactory when recognizing underwater targets with different Doppler shifts, signal-to-noise ratios (SNR), and interferences. In the paper, a one-dimensional convolutional neural network (1D-CNN) was proposed to recognize the line spectrums of Detection of Envelope Modulation on Noise (DEMON) spectrums of underwater target-radiated noise. Datasets

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