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Journal articles on the topic 'One dimensional convolution neural networks'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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Tian, Feng, Shiao Zhang, Miao Cao, and Xiaojun Huang. "Research on accelerated coding absorber design with deep learning." Physica Scripta 98, no. 9 (2023): 096003. http://dx.doi.org/10.1088/1402-4896/acf00a.

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Abstract The traditional design of metamaterials requires a large amount of prior knowledge in electromagnetism and is time-consuming and labour-intensive, but these challenges can be addressed by using trained neural networks to accelerate the forward design process. However, when it comes to coded absorbers, there is no clear ‘guidance manual’ on which neural network is most effective for this task. In this paper, three basic neural networks (full connection, one-dimensional convolution and two-dimensional convolution) are designed considering the apparent pattern and structural parameters o

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Liu, Zhizhe, Luo Sun, and Qian Zhang. "High Similarity Image Recognition and Classification Algorithm Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2836486.

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Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train

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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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Lan, Weichao, and Liang Lan. "Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8235–42. http://dx.doi.org/10.1609/aaai.v35i9.17002.

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Deep Convolutional Neural Networks (CNN) have been successfully applied to many real-life problems. However, the huge memory cost of deep CNN models poses a great challenge of deploying them on memory-constrained devices (e.g., mobile phones). One popular way to reduce the memory cost of deep CNN model is to train binary CNN where the weights in convolution filters are either 1 or -1 and therefore each weight can be efficiently stored using a single bit. However, the compression ratio of existing binary CNN models is upper bounded by ∼ 32. To address this limitation, we propose a novel method

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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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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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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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Xu, Yangyang, Jun Cheng, Lei Wang, Haiying Xia, Feng Liu, and Dapeng Tao. "Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition." IEEE Signal Processing Letters 25, no. 7 (2018): 1044–48. http://dx.doi.org/10.1109/lsp.2018.2841649.

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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.

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Chen, Xiao, and Guoliang Yuan. "Sports Injury Rehabilitation Intervention Algorithm Based on Visual Analysis Technology." Mobile Information Systems 2021 (May 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/9993677.

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Sports injuries of high-level athletes restrict the improvement of sports performance. Under this premise, an efficient and accurate sports injury assessment method is needed to detect potential sports injuries and conduct injury prevention training. Therefore, this paper proposes a novel sports injury prediction algorithm based on visual analysis technology. The proposed algorithm first takes the time-frequency of sensed data as the convolutional neural network (CNN) input. The one-dimensional time series collected by the sensor is converted into two-dimensional images using the Gram angle do

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Hou, Xukun, Pengjie Hu, Wenliao Du, Xiaoyun Gong, Hongchao Wang, and Fannian Meng. "Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network." IOP Conference Series: Materials Science and Engineering 1207, no. 1 (2021): 012003. http://dx.doi.org/10.1088/1757-899x/1207/1/012003.

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Abstract Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural netw

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Chen, Danmin, Zhiqiang Zhang, Funa Zhou, and Chaoge Wang. "A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning." Entropy 26, no. 12 (2024): 1007. http://dx.doi.org/10.3390/e26121007.

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A convolutional neural network can extract features from high-dimensional data, but the convolution operation has a high time complexity and requires a large amount of computation. For equipment with a high sampling frequency, fault diagnosis methods based on convolutional neural networks cannot meet the requirements of online fault diagnosis. To solve this problem, this study proposes a fault diagnosis method for multi-source heterogeneous information fusion based on two-level transfer learning. This method aims to fully utilize multi-source heterogeneous information and external domain data,

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Dan, Yongping, Zhida Wang, Hengyi Li, and Jintong Wei. "Sa-SNN: spiking attention neural network for image classification." PeerJ Computer Science 10 (November 25, 2024): e2549. http://dx.doi.org/10.7717/peerj-cs.2549.

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Spiking neural networks (SNNs) are known as third generation neural networks due to their energy efficient and low power consumption. SNNs have received a lot of attention due to their biological plausibility. SNNs are closer to the way biological neural systems work by simulating the transmission of information through discrete spiking signals between neurons. Influenced by the great potential shown by the attention mechanism in convolutional neural networks, Therefore, we propose a Spiking Attention Neural Network (Sa-SNN). The network includes a novel Spiking-Efficient Channel Attention (SE

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Yin, Yirui. "An ECAPA-TDNN Based Network for Hand Gesture Recognition on Skeletal Data." Highlights in Science, Engineering and Technology 68 (October 9, 2023): 366–73. http://dx.doi.org/10.54097/hset.v68i.12502.

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Due to the high variety of sign languages, it is essential to present a model that could recognize the hand gesture recognition. The state-of-art model is mainly driven by convolution neural networks (known as CNN), and researches are on optimizing CNN architectures. The CNN networks are too large and require long time to train. To address these challenges, we developed a more accurate and robust ECAPA-TDNN structure for recognition. The ECAPA-TDNN is a structure of multiple one- dimensional neural networks with one-dimensional convolution, activation layers, and batch normalization. On the ch

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Liang, Yu-Pei, Hao Chen, and Ching-Che Chung. "A One-Dimensional Depthwise Separable Convolutional Neural Network for Bearing Fault Diagnosis Implemented on FPGA." Sensors 24, no. 23 (2024): 7831. https://doi.org/10.3390/s24237831.

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This paper presents a hardware implementation of a one-dimensional convolutional neural network using depthwise separable convolution (DSC) on the VC707 FPGA development board. The design processes the one-dimensional rolling bearing current signal dataset provided by Paderborn University (PU), employing minimal preprocessing to maximize the comprehensiveness of feature extraction. To address the high parameter demands commonly associated with convolutional neural networks (CNNs), the model incorporates DSC, significantly reducing computational complexity and parameter load. Additionally, the

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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 th

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Zhang, Lishan, Lei Han, Yuzhen Meng, and Wenkui Zhao. "Multi-input Convolutional Neural Network Fault Diagnosis Algorithm Based on the Hydraulic Pump." Journal of Physics: Conference Series 2095, no. 1 (2021): 012069. http://dx.doi.org/10.1088/1742-6596/2095/1/012069.

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Abstract Convolutional neural network used in fault diagnosis can effectively extract fault features in vibration signals. However, in the feature extraction of mechanical fault diagnosis, usually more than two feature signals including at least axial and radial vibration signals can be extracted. This paper proposes two multi-input convolutional neural network models based on the fault data of the aircraft hydraulic pump including axial and radial vibration. The first is the Independent Input Multi-input Convolutional Neural Network model. The two inputs are respectively used for convolution

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Wang, Baiyang, Yidong Xu, Siyu Peng, Hongjun Wang, and Fang Li. "Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks." Sensors 24, no. 11 (2024): 3360. http://dx.doi.org/10.3390/s24113360.

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Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which use

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Wang, Yangyang, Shuzhan Huang, Juying Dai, and Jian Tang. "A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network." Shock and Vibration 2020 (January 30, 2020): 1–17. http://dx.doi.org/10.1155/2020/1850286.

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This paper constructs a novel network structure (SVD-1DCNN) based on singular value decomposition (SVD) and one-dimensional convolutional neural network (1DCNN), which takes the original signal as input to realize intelligent diagnosis of bearing faults. The output of the first convolution layer was also analyzed from the perspectives of time domain and time-frequency domain in the simulation experiment. Through qualitative analysis and quantitative analysis, it was found that the convolution kernel not only extracted the classification features of signals but also gradually highlighted the le

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WAN, XIANGKUI, ZHIYAO JIN, HAIBO WU, JUNJIE LIU, BINRU ZHU, and HONGGANG XIE. "HEARTBEAT CLASSIFICATION ALGORITHM BASED ON ONE-DIMENSIONAL CONVOLUTION NEURAL NETWORK." Journal of Mechanics in Medicine and Biology 20, no. 07 (2020): 2050046. http://dx.doi.org/10.1142/s0219519420500463.

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The morbidity of cardiovascular disease increasingly rises, which makes great impact upon people’s health and life. Electrocardiogram (ECG) beat classification is of great significance to clinical diagnosis of cardiovascular diseases. Traditional ECG signal classification algorithm relies heavily on the accuracy of feature extraction or increases the complexity of the calculation process by means of the correlation characteristic coefficient transformation, which results in that the ECG beat classification effect is still not satisfactory. Aimed at this problem, a novel method based on convolu

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Guan, Wenhui, and Binbin Li. "Research on diagnosis method of motor vibration signal based on MSCNN-LSTM." Journal of Physics: Conference Series 2816, no. 1 (2024): 012035. http://dx.doi.org/10.1088/1742-6596/2816/1/012035.

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Abstract Vibration signal is often considered an important basis for diagnosing motor faults. However, the original vibration signal features a single time series that needs to be shorter. This paper introduces a fault diagnosis approach, MSCNN-LSTM, which integrates a multi-scale one-dimensional convolutional neural network with a long short-term memory network, reflecting the ongoing advancements in deep learning for fault diagnosis. Convolution kernels of varying sizes are accustomed to realizing information integration of various scales and broadening the dimensions of vibration signals. I

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Kang, Byungjin, Inho Park, Changmin Ok, and Sungho Kim. "ODPA-CNN: One Dimensional Parallel Atrous Convolution Neural Network for Band-Selective Hyperspectral Image Classification." Applied Sciences 12, no. 1 (2021): 174. http://dx.doi.org/10.3390/app12010174.

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Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information. These methods can increase the accuracy of classification, but do not only focus on the spectral information, which is a big advantage of HSI. In addition, the 1D-CNN, which learns only pure spectral information, has limitations because it uses adjacent spectral information. In this paper, we propose a One Dimensional Parellel Atrous Convolution Neural Network (ODPA-CNN) that

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Du, Huipeng, Gang Wang, and Jiazhao Li. "Transformer Fault Identification with an IF-1DCNN Based on Informative Integration of Heterogeneous Sources." Mathematical Problems in Engineering 2021 (February 20, 2021): 1–14. http://dx.doi.org/10.1155/2021/6648919.

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Only using single feature information as input feature cannot fully reflect the transformer fault classification and improve the accuracy of transformer fault diagnosis. To address the above problem, the convolution neural networks’ model is applied for transformer fault assessment designed to implement an end-to-end “different space feature extraction + transformer state diagnosis classification” to enable information from possibly heterogeneous sources to be integrated. This method integrates various feature information of the power transformer operation state to form the isomeric feature, a

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Reza, Selim, Marta Campos Ferreira, José J. M. Machado, and João Manuel R. S. Tavares. "Traffic State Prediction Using One-Dimensional Convolution Neural Networks and Long Short-Term Memory." Applied Sciences 12, no. 10 (2022): 5149. http://dx.doi.org/10.3390/app12105149.

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Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non-linear behaviour. With the availability of a vast number of data samples, deep neural network-based models are best suited to solve these challenges. However, conventional network-based models lack robustness and accuracy because of their incapability to capture traffic’s spatial and temporal corre

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Li, Yanxi, Zean Wen, Yunhe Wang, and Chang Xu. "One-shot Graph Neural Architecture Search with Dynamic Search Space." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8510–17. http://dx.doi.org/10.1609/aaai.v35i10.17033.

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Relying on the diverse graph convolution operations that have emerged in recent years, graph neural networks (GNNs) are shown to be powerful to deal with high-dimensional non-Euclidean domains, such as social networks or citation networks. Despite the tremendous human efforts been taken to explore new graph convolution operations, there are a few attempts to automatically search operations in GNNs. The search space of GNNs is significantly larger than that of CNNs, because of diverse components in the message-passing of GNNs. This, therefore, prevents the straightforward application of classic

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Wu, Haiyang, Hui Zhou, Chang Liu, Gang Cheng, and Yusong Pang. "Fault Diagnosis Method for Shearer Arm Gear Based on Improved S-Transform and Depthwise Separable Convolution." Sensors 25, no. 13 (2025): 4067. https://doi.org/10.3390/s25134067.

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To address the limitations in time–frequency feature representation of shearer arm gear faults and the issues of parameter redundancy and low training efficiency in standard convolutional neural networks (CNNs), this study proposes a diagnostic method based on an improved S-transform and a Depthwise Separable Convolutional Neural Network (DSCNN). First, the improved S-transform is employed to perform time–frequency analysis on the vibration signals, converting the original one-dimensional signals into two-dimensional time–frequency images to fully preserve the fault characteristics of the gear

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Xu, Huafen, and Xiwen Zhang. "Recognizing Digital Ink Chinese Characters Written by International Students Using a Residual Network with 1-Dimensional Dilated Convolution." Information 15, no. 9 (2024): 531. http://dx.doi.org/10.3390/info15090531.

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Due to the complex nature of Chinese characters, junior international students often encounter writing problems related to strokes, components, and their combinations when writing Chinese characters. Digital ink Chinese characters (DICCs) are obtained by sampling the writing trajectory of Chinese characters with a pen input device. DICCs contain rich information, such as the time and space of strokes and sampling points. Recognizing DICCs is crucial for evaluating and correcting writing errors and enhancing the quality of Chinese character teaching for international students. Here, the paper f

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Zhang, Kedong. "Music Style Classification Algorithm Based on Music Feature Extraction and Deep Neural Network." Wireless Communications and Mobile Computing 2021 (September 4, 2021): 1–7. http://dx.doi.org/10.1155/2021/9298654.

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The music style classification technology can add style tags to music based on the content. When it comes to researching and implementing aspects like efficient organization, recruitment, and music resource recommendations, it is critical. Traditional music style classification methods use a wide range of acoustic characteristics. The design of characteristics necessitates musical knowledge and the characteristics of various classification tasks are not always consistent. The rapid development of neural networks and big data technology has provided a new way to better solve the problem of musi

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Salimov, Boris, Oleg Berngardt, Aleksey Hmelnov, Konstantin Ratovsky та Oleg Kusonsky. "Application of convolution neural networks for critical frequency fₒF2 prediction". Solar-Terrestrial Physics 9, № 1 (2023): 56–67. http://dx.doi.org/10.12737/stp-91202307.

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Ionosphere has an important impact on the quality of radio communication, radar, and global positioning. One of the essential characteristics describing the state of the ionosphere is its critical frequency fₒF2. Its prediction provides effective modes of operation of technical radio equipment as well as enables calculation of the corrections needed to improve the accuracy of its functioning. Different physical and empirical models are generally used for fₒF2 prediction. This paper proposes an empirical prediction technique based on machine learning methods and observational history. It relies

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Salimov, Boris, Oleg Berngardt, Aleksey Hmelnov, Konstantin Ratovsky та Oleg Kusonsky. "Application of convolution neural networks for critical frequency fₒF2 prediction". Solnechno-Zemnaya Fizika 9, № 1 (2023): 60–72. http://dx.doi.org/10.12737/szf-91202307.

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Ionosphere has an important impact on the quality of radio communication, radar, and global positioning. One of the essential characteristics describing the state of the ionosphere is its critical frequency fₒF2. Its prediction provides effective modes of operation of technical radio equipment as well as enables calculation of the corrections needed to improve the accuracy of its functioning. Different physical and empirical models are generally used for fₒF2 prediction. This paper proposes an empirical prediction technique based on machine learning methods and observational history. It relies

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Rifai, Ahmad, Muhammad Naufal Rachmamtullah, Sutarno Sutarno, and Bambang Tutuko. "Inter Patient Atrial Fibrillation Classification Using One Dimensional Convolution Neural Network." Computer Engineering and Applications Journal 11, no. 1 (2022): 51–61. http://dx.doi.org/10.18495/comengapp.v11i1.393.

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Atrial fibrillation is the most common type of arrhythmia. The process of detecting AF disease is quite difficult. This is because it is necessary to detect the presence or absence of a P signal wave in the ECG signal. However, this method requires special expertise from a cardiologist. Several literatures have proposed an automatic ECG classification system. However, the intra-patient paradigm does not simulate real-world scenarios. One of the challenges in the inter-patient paradigm is the morphological differences between one subject and another. In order to overcome the problems that arise

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Xu, Chuanyu, and Wei Xu. "Causal Structure Learning With One-Dimensional Convolutional Neural Networks." IEEE Access 9 (2021): 162147–55. http://dx.doi.org/10.1109/access.2021.3133496.

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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

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Lu, Gang, Yuanbin Wang, Huayong Yang, and Jun Zou. "One-dimensional convolutional neural networks for acoustic waste sorting." Journal of Cleaner Production 271 (October 2020): 122393. http://dx.doi.org/10.1016/j.jclepro.2020.122393.

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Malek, Salim, Farid Melgani, and Yakoub Bazi. "One-dimensional convolutional neural networks for spectroscopic signal regression." Journal of Chemometrics 32, no. 5 (2017): e2977. http://dx.doi.org/10.1002/cem.2977.

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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 o

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Zhang, Lei, Xiangqian Ding, and Ruichun Hou. "Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks." Journal of Analytical Methods in Chemistry 2020 (February 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/9652470.

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The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the patter

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Shen, Kenan, and Dongbiao Zhao. "Fault Diagnosis for Aircraft Hydraulic Systems via One-Dimensional Multichannel Convolution Neural Network." Actuators 11, no. 7 (2022): 182. http://dx.doi.org/10.3390/act11070182.

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Detecting the faults in hydraulic systems in advance is difficult owing to the complexity associated with such systems. Hence, it is necessary to investigate the different fault modes and analyze the system reliability in order to establish a method for improving the reliability and security of hydraulic systems. To this end, this paper proposes a novel one-dimensional multichannel convolution neural network (1DMCCNN) for diagnosing fault modes. In this work, a landing gear hydraulic system was constructed with a normal model and a fault model; five types of faults were considered. Pressure si

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Zhang, Ruixing, Tao Yao, and Lianshan Yan. "Feature Point Detection and Description Networks Based on Asymmetric Convolution and the Cross-ResolutionImage-Matching Method." International Journal of Intelligent Systems 2023 (February 20, 2023): 1–15. http://dx.doi.org/10.1155/2023/5131440.

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Image matching can be transformed into the problem of feature point detection and matching of images. The current neural network methods have a weak detection effect on feature points and cannot extract enough sparse and uniform feature points. In order to improve the detection and description ability of feature points, this paper proposes a self-supervised feature point detection and description network based on asymmetric convolution: ACPoint. Specifically, first, feature point pseudolabels are learned from an unlabeled dataset, and pseudolabels are used for supervised learning; then, the le

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Yang, Aoyu, Zhidan Zhong, Yao Zhao, and Zhihui Zhang. "Rolling bearing fault diagnosis method based on dynamic convolution capsule network." Journal of Physics: Conference Series 2419, no. 1 (2023): 012091. http://dx.doi.org/10.1088/1742-6596/2419/1/012091.

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Abstract Aiming at the weak generalization ability of traditional rolling bearing fault diagnosis methods, a rolling bearing diagnosis method based on dynamic convolutional capsule networks (DC-CapNets) was proposed. First, one-dimensional vibration signals are preprocessed and divided into a training set and a test set. Then the fast Fourier transform (FFT) is used to convert the training set and the test set. In this model, two dynamic convolution layers and pooling layers are used to extract the features of the input frequency signals, and the feature information is transmitted to the capsu

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Jian, Xianzhong, Wenlong Li, Xuguang Guo, and Ruzhi Wang. "Fault Diagnosis of Motor Bearings Based on a One-Dimensional Fusion Neural Network." Sensors 19, no. 1 (2019): 122. http://dx.doi.org/10.3390/s19010122.

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Deep learning has been an important topic in fault diagnosis of motor bearings, which can avoid the need for extensive domain expertise and cumbersome artificial feature extraction. However, existing neural networks have low fault recognition rates and low adaptability under variable load conditions. In order to solve these problems, we propose a one-dimensional fusion neural network (OFNN), which combines Adaptive one-dimensional Convolution Neural Networks with Wide Kernel (ACNN-W) and Dempster-Shafer (D-S) evidence theory. Firstly, the original vibration time-domain signals of a motor beari

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Sim, Hee-Dong, Mi-Ri Jeong, Mi-Hyun Lee, and Seok-Jo Yang. "Classification of EEG for Dementia Patients with One-Dimensional Convolution Neural Network." Transactions of the Korean Society of Mechanical Engineers - B 44, no. 4 (2020): 237–44. http://dx.doi.org/10.3795/ksme-b.2020.44.4.237.

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Xiao, Yao, and Xi zhang Wei. "Specific emitter identification of radar based on one dimensional convolution neural network." Journal of Physics: Conference Series 1550 (May 2020): 032114. http://dx.doi.org/10.1088/1742-6596/1550/3/032114.

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Yan, Jun, Junxia Meng, and Jianhu Zhao. "Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks." Remote Sensing 12, no. 1 (2019): 37. http://dx.doi.org/10.3390/rs12010037.

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As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper,

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Li, Xin, Hong Huang, Guotao Yuan, Zhaolian Wang, and Rui Du. "An Intrusion Detection Method based on Fusion Neural Network." Frontiers in Computing and Intelligent Systems 4, no. 2 (2023): 124–30. http://dx.doi.org/10.54097/fcis.v4i2.10369.

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Aiming at the problems of class imbalance, insufficient feature learning, weak generalization ability, and representation capability in existing intrusion detection models, we propose a multi-scale feature fusion Intrusion Detection Model (MSFF). This model combines multi-scale one-dimensional convolution and bidirectional long short-term memory (LSTM) networks, and incorporates residual connections with identity mappings to address the problem of network degradation. The multi-scale convolution captures feature representations at different levels, thereby improving the expressive power of the

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Hamed Mozaffari, M., and Li-Lin Tay. "Overfitting One-Dimensional convolutional neural networks for Raman spectra identification." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 272 (May 2022): 120961. http://dx.doi.org/10.1016/j.saa.2022.120961.

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Li, Ruixue, Bo Yin, Yanping Cong, and Zehua Du. "Simultaneous Prediction of Soil Properties Using Multi_CNN Model." Sensors 20, no. 21 (2020): 6271. http://dx.doi.org/10.3390/s20216271.

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Soil nutrient prediction based on near-infrared spectroscopy has become the main research direction for rapid acquisition of soil information. The development of deep learning has greatly improved the prediction accuracy of traditional modeling methods. In view of the low efficiency and low accuracy of current soil prediction models, this paper proposes a soil multi-attribute intelligent prediction method based on convolutional neural networks, by constructing a dual-stream convolutional neural network model Multi_CNN that combines one-dimensional convolution and two-dimensional convolution, t

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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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Xue, Yingfang, Chaozhi Cai, and Yaolei Chi. "Frame Structure Fault Diagnosis Based on a High-Precision Convolution Neural Network." Sensors 22, no. 23 (2022): 9427. http://dx.doi.org/10.3390/s22239427.

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Structural health monitoring and fault diagnosis are important scientific issues in mechanical engineering, civil engineering, and other disciplines. The basic premise of structural health work is to be able to accurately diagnose the fault in the structure. Therefore, the accurate fault diagnosis of structure can not only ensure the safe operation of mechanical equipment and the safe use of civil construction, but also ensure the safety of people’s lives and property. In order to improve the accuracy fault diagnosis of frame structure under noise conditions, the existing Convolutional Neural

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