Relevant bibliographies by topics / 1D CNN

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic '1D CNN'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "1D CNN"

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Hsieh, Tien-Heng, and Jean-Fu Kiang. "Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands." Sensors 20, no. 6 (March 20, 2020): 1734. http://dx.doi.org/10.3390/s20061734.

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Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data.
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Ghafoor, Karzan J., Karwan M. Hama Rawf, Ayub O. Abdulrahman, and Sarkhel H. Taher. "Kurdish Dialect Recognition using 1D CNN." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, no. 2 (October 15, 2021): 10–14. http://dx.doi.org/10.14500/aro.10837.

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Dialect recognition is one of the most attentive topics in the speech analysis area. Machine learning algorithms have been widely used to identify dialects. In this paper, a model that based on three different 1D Convolutional Neural Network (CNN) structures is developed for Kurdish dialect recognition. This model is evaluated, and CNN structures are compared to each other. The result shows that the proposed model has outperformed the state of the art. The model is evaluated on the experimental data that have been collected by the staff of department of computer science at the University of Halabja. Three dialects are involved in the dataset as the Kurdish language consists of three major dialects, namely Northern Kurdish (Badini variant), Central Kurdish (Sorani variant), and Hawrami. The advantage of the CNN model is not required to concern handcraft as the CNN model is featureless. According to the results, the 1 D CNN method can make predictions with an average accuracy of 95.53% on the Kurdish dialect classification. In this study, a new method is proposed to interpret the closeness of the Kurdish dialects by using a confusion matrix and a non-metric multi-dimensional visualization technique. The outcome demonstrates that it is straightforward to cluster given Kurdish dialects and linearly isolated from the neighboring dialects.
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Kim, A. Ran, Ha Seon Kim, Chang Ho Kang, and Sun Young Kim. "The Design of the 1D CNN–GRU Network Based on the RCS for Classification of Multiclass Missiles." Remote Sensing 15, no. 3 (January 18, 2023): 577. http://dx.doi.org/10.3390/rs15030577.

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For real-time target classification, a study was conducted to improve the AI-based target classification performance using RCS measurements that are vulnerable to noise, but can be obtained quickly. To compensate for the shortcomings of the RCS, a 1D CNN–GRU network with strengths in feature extraction and time-series processing was considered. The 1D CNN–GRU was experimentally changed and designed to fit the RCS characteristics. The performance of the proposed 1D CNN–GRU was compared and analyzed using the 1D CNN and 1D CNN–LSTM. The designed 1D CNN–GRU had the best classification performance with a high accuracy of 99.50% in complex situations, such as with different missile shapes with the same trajectory and with the same missile shapes that had the same trajectory. In addition, to confirm the general target classification performance for the RCS, a new class was verified. The 1D CNN–GRU had the highest classification performance at 99.40%. Finally, as a result of comparing three networks by adding noise to compensate for the shortcomings of the RCS, the 1D CNN–GRU, which was optimized for both the data set used in this paper and the newly constructed data set, was the most robust to noise.
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Ma, Xiaotong, Qixia Man, Xinming Yang, Pinliang Dong, Zelong Yang, Jingru Wu, and Chunhui Liu. "Urban Feature Extraction within a Complex Urban Area with an Improved 3D-CNN Using Airborne Hyperspectral Data." Remote Sensing 15, no. 4 (February 10, 2023): 992. http://dx.doi.org/10.3390/rs15040992.

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Airborne hyperspectral data has high spectral-spatial information. However, how to mine and use this information effectively is still a great challenge. Recently, a three-dimensional convolutional neural network (3D-CNN) provides a new effective way of hyperspectral classification. However, its capability of data mining in complex urban areas, especially in cloud shadow areas has not been validated. Therefore, a 3D-1D-CNN model was proposed for feature extraction in complex urban with hyperspectral images affected by cloud shadows. Firstly, spectral composition parameters, vegetation index, and texture characteristics were extracted from hyperspectral data. Secondly, the parameters were fused and segmented into many S × S × B patches which would be input into a 3D-CNN classifier for feature extraction in complex urban areas. Thirdly, Support Vector Machine (SVM), Random Forest (RF),1D-CNN, 3D-CNN, and 3D-2D-CNN classifiers were also carried out for comparison. Finally, a confusion matrix and Kappa coefficient were calculated for accuracy assessment. The overall accuracy of the proposed 3D-1D-CNN is 96.32%, which is 23.96%, 11.02%, 5.22%, and 0.42%, much higher than that of SVM, RF, 1D-CNN, or 3D-CNN, respectively. The results indicated that 3D-1D-CNN could mine spatial-spectral information from hyperspectral data effectively, especially that of grass and highway in cloud shadow areas with missing spectral information. In the future, 3D-1D-CNN could also be used for the extraction of urban green spaces.
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Hou, Wenguang, Shaojie Mei, Qiuling Gui, Yingcheng Zou, Yifan Wang, Xianbo Deng, and Qimin Cheng. "1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA." Complexity 2020 (November 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/7023754.

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How to automatically detect intracranial aneurysms from Three-Dimension Time of Flight Magnetic Resonance Angiography (3D TOF MRA) images is a typical 3D image classification problem. Currently, the commonly used method is the Maximum Intensity Projection- (MIP-) based way. It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. After then, the 2D Convolutional Neural Network (CNN) is established to do classification. It has been shown that the MIP-based method can reduce the demands for the samples and increase the computation efficiency. Meanwhile, the accuracy is comparable with that of 3D image classification. Inspired by the strategy of MIP, we want to further reduce the demands for samples and accelerate the training by transferring the 2D image classification into 1D case, i.e., we want to generate the 1D vectors from the MIP images and then establish a 1D CNN to do intracranial aneurysm detection and classification for 3D TOF MRA image. Specifically, our method first extracts a series of patches as the Region of Interests (ROIs) along the blood vessels from the original 3D TOF MRA 3D image. The corresponding MIP images of each ROI will be obtained through maximum intensity projecting. Then, we generate a series of 1D vectors by accumulating each MIP image along different directions. Meanwhile, a 1D CNN is established to detect aneurysms, in which, the input is the obtained 1D vectors and the output is the binary classification result denoting whether there are intracranial aneurysms in the considered patch. Generally, compared with 2D- and 3D-CNN, the 1D CNN-based way greatly accelerates the training and shows stronger robustness in the case of fewer samples. The efficiency of the proposed method outperforms the 2D CNN about 10 times in CPU training. Yet, their accuracies are close.
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Li, Xingpeng, Hongzhe Jiang, Xuesong Jiang, and Minghong Shi. "Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm." Agriculture 11, no. 12 (December 15, 2021): 1274. http://dx.doi.org/10.3390/agriculture11121274.

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The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts.
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Yuan, Xinzhe, Dustin Tanksley, Liujun Li, Haibin Zhang, Genda Chen, and Donald Wunsch. "Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks." Applied Sciences 11, no. 21 (October 21, 2021): 9844. http://dx.doi.org/10.3390/app11219844.

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Contemporary deep learning approaches for post-earthquake damage assessments based on 2D convolutional neural networks (CNNs) require encoding of ground motion records to transform their inherent 1D time series to 2D images, thus requiring high computing time and resources. This study develops a 1D CNN model to avoid the costly 2D image encoding. The 1D CNN model is compared with a 2D CNN model with wavelet transform encoding and a feedforward neural network (FNN) model to evaluate prediction performance and computational efficiency. A case study of a benchmark reinforced concrete (r/c) building indicated that the 1D CNN model achieved a prediction accuracy of 81.0%, which was very close to the 81.6% prediction accuracy of the 2D CNN model and much higher than the 70.8% prediction accuracy of the FNN model. At the same time, the 1D CNN model reduced computing time by more than 90% and reduced resources used by more than 69%, as compared to the 2D CNN model. Therefore, the developed 1D CNN model is recommended for rapid and accurate resultant damage assessment after earthquakes.
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Byun, Eunseok, and Jongsoo Lee. "Image-based Vibration Signal Measurement and Calibration Using 1D CNN." Transactions of the Korean Society of Mechanical Engineers - A 46, no. 8 (August 31, 2022): 765–72. http://dx.doi.org/10.3795/ksme-a.2022.46.8.765.

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Liu, Yan, Yue Shen, Li Li, and Hai Wang. "FPGA Implementation of a BPSK 1D-CNN Demodulator." Applied Sciences 8, no. 3 (March 15, 2018): 441. http://dx.doi.org/10.3390/app8030441.

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Kim, Jung-Woo, Seung-Ho Park, Sock-Kyu Lee, and Kyoung-Su Park. "Artificial Intelligence Network with 1D-/2D-CNN and LSTM Predicting Flank Wear from Raw Vibration Signals." Transactions of the Korean Society for Noise and Vibration Engineering 32, no. 4 (August 31, 2022): 384–91. http://dx.doi.org/10.5050/ksnve.2022.32.4.384.

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Dissertations / Theses on the topic "1D CNN"

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Al-Kadhimi, Staffan, and Paul Löwenström. "Identification of machine-generated reviews : 1D CNN applied on the GPT-2 neural language model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280335.

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With recent advances in machine learning, computers are able to create more convincing text, creating a concern for an increase in fake information on the internet. At the same time, researchers are creating tools for detecting computer-generated text. Researchers have been able to exploit flaws in neural language models and use them against themselves; for example, GLTR provides human users with a visual representation of texts that assists in classification as human-written or machine-generated. By training a convolutional neural network (CNN) on GLTR output data from analysis of machine-generated and human-written movie reviews, we are able to take GLTR a step further and use it to automatically perform this classification. However, using a CNN with GLTR as the main source of data for classification does not appear to be enough to be on par with the best existing approaches.
I och med de senaste framstegen inom maskininlärning kan datorer skapa mer och mer övertygande text, vilket skapar en oro för ökad falsk information på internet. Samtidigt vägs detta upp genom att forskare skapar verktyg för att identifiera datorgenererad text. Forskare har kunnat utnyttja svagheter i neurala språkmodeller och använda dessa mot dem. Till exempel tillhandahåller GLTR användare en visuell representation av texter, som hjälp för att klassificera dessa som människo- skrivna eller maskingenererade. Genom att träna ett faltningsnätverk (convolutional neural network, eller CNN) på utdata från GLTR-analys av maskingenererade och människoskrivna filmrecensioner, tar vi GLTR ett steg längre och använder det för att genomföra klassifikationen automatiskt. Emellertid tycks det ej vara tillräckligt att använda en CNN med GLTR som huvuddatakälla för att klassificera på en nivå som är jämförbar med de bästa existerande metoderna.
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Ghibellini, Alessandro. "Trend prediction in financial time series: a model and a software framework." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24708/.

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The research has the aim to build an autonomous support for traders which in future can be translated in an Active ETF. My thesis work is characterized for a huge focus on problem formulation and an accurate analysis on the impact of the input and the length of the future horizon on the results. I will demonstrate that using financial indicators already used by professional traders every day and considering a correct length of the future horizon, it is possible to reach interesting scores in the forecast of future market states, considering both accuracy, which is around 90% in all the experiments, and confusion matrices which confirm the good accuracy scores, without an expensive Deep Learning approach. In particular, I used a 1D CNN. I also emphasize that classification appears to be the best approach to address this type of prediction in combination with proper management of unbalanced class weights. In fact, it is standard having a problem of unbalanced class weights, otherwise the model will react for inconsistent trend movements. Finally I proposed a Framework which can be used also for other fields which allows to exploit the presence of the Experts of the sector and combining this information with ML/DL approaches.
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Bosello, Michael. "Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21467/.

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Recent breakthroughs in machine learning are paving the way to the vision of software 2.0 era, which foresees the replacement of traditional software development with such techniques for many applications. In the context of agent-oriented programming, we believe that mixing together cognitive architectures like the BDI one and learning techniques could trigger new interesting scenarios. In that view, our previous work presents Jason-RL, a framework that integrates BDI agents and Reinforcement Learning (RL) more deeply than what has been already proposed so far in the literature. The framework allows the development of BDI agents having both explicitly programmed plans and plans learned by the agent using RL. The two kinds of plans are seamlessly integrated and can be used without differences. Here, we take autonomous driving as a case study to verify the advantages of the proposed approach and framework. The BDI agent has hard-coded plans that define high-level directions while fine-grained navigation is learned by trial and error. This approach – compared to plain RL – is encouraging as RL struggles in temporally extended planning. We defined and trained an agent able to drive in a track with an intersection, at which it has to choose the correct path to reach the assigned target. A first step towards porting the system in the real-world has been done by building a 1/10 scale racecar prototype which learned how to drive in a simple track.
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Bouillonnec, Jonathan. "Elaboration et étude des propriétés mécaniques et thermiques de matériaux constitués de nanotubes de carbone verticalement alignés." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30228/document.

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Les tapis de nanotubes de carbone verticalement alignés sont des candidats potentiels pour des applications telles que les interconnexions ou les matériaux d'interface thermique. Ce travail de recherche porte sur la synthèse de tapis de nanotubes de carbone alignés selon le procédé de dépôt chimique en phase vapeur (CVD) d'aérosols liquides, sur l'élaboration de nanocomposites constitués de différentes nuances de matrices époxy infiltrées au sein de ces tapis, ainsi que sur l'étude des propriétés mécaniques et thermiques longitudinales et transverses des tapis secs eux-mêmes et des nanocomposites 1D formés. Les conditions de synthèse permettent notamment de faire varier les caractéristiques des tapis telles que leur épaisseur, leur masse volumique, le diamètre externe moyen des nanotubes de carbone (NTC), l'espace intertube et la teneur volumique en NTC, alors que leur structure cristalline peut être modifiée par le biais d'un traitement thermique à haute température. L'objectif principal de ce travail consiste à démontrer et quantifier l'effet de certaines caractéristiques des tapis de nanotubes de carbone sur les propriétés mécaniques et thermiques des différents types de tapis et matériaux composites obtenus. Les deux méthodes d'imprégnation mises en oeuvre, voie liquide et infusion, conduisent à des tapis de NTC alignés denses avec un alignement des NTC conservé et une répartition homogène des NTC au sein du système époxy. La fraction volumique en NTC s'avère être le paramètre-clé permettant d'exacerber, dans la direction longitudinale aux NTC, les propriétés mécaniques et thermiques des nanocomposites. Par ailleurs, les tapis de NTC et les nanocomposites voient leurs propriétés de conduction thermique longitudinale nettement exacerbées lorsque les NTC présentent une amélioration de leur structure cristalline. L'augmentation significative des performances apportées par les tapis de NTC verticalement alignés au sein de ces matériaux nanocomposites anisotropes par rapport aux matrices organiques non chargées est prometteuse et ouvre des pistes de réflexion visant à répondre aux nouvelles exigences de multifonctionnalité des secteurs de l'aéronautique et de l'aérospatial
Vertically aligned carbon nanotube carpets are potential candidates for applications such as interconnections or thermal interface materials (TIMs). This research work deals with the synthesis of aligned carbon nanotube carpets from the aerosol assisted chemical vapour deposition (CVD) technique, with the elaboration of nanocomposites made of different grades of epoxy matrix infiltrated within these carpets, as well as the study of both longitudinal and transverse mechanical and thermal properties of dry carpets themselves and 1D-nanocomposites separately. The synthesis conditions notably enable to vary characteristics of the differents carpets such as their thickness, their density, the mean external diameter of the carbon nanotubes (CNT), the intertube space and the CNT volume fraction, whereas their crystalline structure can be modified with a high temperature thermal treatment. The main goal of this work is to prove and quantify the effect of some of the characteristics of the carbon nanotubes carpets on both mechanical and thermal properties of the different kinds of CNT carpets and resulting composite materials. The two impregnation methods used, liquid way and infusion, lead to dense CNT carpets with a preserved alignment of the CNT and an homogeneous distribution of these latest within the epoxy system. The CNT volume content is evidenced as the key-parameter exacerbating the mechanical and thermal properties mainly in the longitudinal direction compared with the alignment axis of the CNTs. Moreover the mechanical and thermal conduction properties of the CNT carpets and the 1D-nanocomposites are clearly increased when the crystalline structure of the CNT is improved. The significant increasing of the properties brought by the vertically aligned CNT within these anisotropic 1D-nanocomposites compared with the only organic matrixes is promising and opens new pathways aiming to meet the latest specifications related to multifunctionnality in fields such as aeronautics and aerospace
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Books on the topic "1D CNN"

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Nechaev, Vladimir, Andrey Shuba, Stanislav Gridnev, and Vitaliy Topolov. Dimensional effects in phase transitions and physical properties of ferroics. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1898400.

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The monograph presents mathematical methods and a set of mathematical models describing, within the framework of phenomenological theory, phase transitions in 0D-. 1D-, 2D-, 3D-dimensional ferroelectrics, ferroelastics, ferromagnets and their static and dynamic physical properties near the phase transition point. The influence of the parameters characterizing the ferroic sample and its interaction with the environment on the features of the phase transition, phase transition temperature shift, heat capacity, generalized susceptibilities is analyzed. Mathematical models of multilayer thin-film structures and composite materials, where one of the components is a ferroic nanoparticle, are considered. In general, modern ideas about dimensional effects in ferroelectrics, ferroelastics, ferromagnets and mechanisms of purposeful influence on their properties are sufficiently fully covered. It is intended for researchers, students and postgraduates of physical specialties of universities interested in fundamental problems of formation of physical properties of low-dimensional materials. Research engineers, developers of new materials can use the presented material as a scientific and methodological basis to support the development of optimal solutions for their creation.
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Daghero, D., G. A. Ummarino, and R. S. Gonnelli. Andreev Reflection and Related Studies in Low-Dimensional Superconducting Systems. Edited by A. V. Narlikar. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198738169.013.5.

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This article investigates the potential of the point contact Andreev reflection spectroscopy (PCARS) technique for measuring the symmetry of the energy gap and other key parameters of various 0-, 1-, and 2-dimensional superconducting systems. It begins with a brief description of PCARS, explaining what a point contact is and how it can be made and the conditions under which a PC is ballistic, as well as why and to what extent a PC between normal metals is spectroscopic. It then discusses the basics of Andreev reflection and the length scales in mesoscopic systems before considering the limits of applicability of PCARS for spectroscopy of ‘small’ superconductors. Finally, it reviews some examples of PCARS in quasi-0D, quasi-1D and quasi-2D superconductors.
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Lin, Nian, and Sebastian Stepanow. Designing low-dimensional nanostructures at surfaces by supramolecular chemistry. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533046.013.10.

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This article describes the use of supramolecular chemistry to design low-dimensional nanostructures at surfaces. In particular, it discusses the design strategies of two types of low-dimensional supramolecular nanostructures: structures stabilized by hydrogen bonds and structures stabilized by metal-ligand co-ordination interactions. After providing an overview of hydrogen-bond systems such as 0D discrete clusters, 1D chains, and 2D open networks and close-packed arrays, the article considers metal-co-ordination systems. It also presents experimental results showing that both hydrogen bonds and metal co-ordination offer protocols to achieve unique nanostructured systems on 2D surfaces or interfaces. Noting that the conventional 3D supramolecular self-assembly has generated a vast number of nanostructures revealing high complexity and functionality, the article suggests that 2D approaches can be applied to substrates with different symmetries as well as physical and chemical properties.
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Stamenova, M., and S. Sanvito. Atomistic spin-dynamics. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533046.013.7.

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This article reviews recent advances towards the development of a truly atomistic time-dependent theory for spin-dynamics. The focus is on the s-d tight-binding model [where conduction electrons (s) are exchange-coupled to a number of classical spins (d)], including electrostatic corrections at the Hartree level, as the underlying electronic structure theory. In particular, the article considers one-dimensional (1D) magnetic atomic wires and their electronic structure, described by means of the s-d model. The discussion begins with an overview of the model spin Hamiltonian, followed by molecular-dynamics simulations of spin-wave dispersion in a s-d monoatomic chain and spin impurities in a non-magnetic chain. The current-induced motion in a magnetic domain wall (DW) is also explored, along with how an electric current can affect the magnetization landscape of a magnetic nano-object. The article concludes with an assessment of spin-motive force, and especially whether a driven magnetization dynamics can generate an electrical signal.
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Book chapters on the topic "1D CNN"

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Jacob, Jemia Anna, Jestin P. Cherian, Joseph George, Christo Daniel Reji, and Divya Mohan. "Heart Diseases Classification Using 1D CNN." In Advances in Intelligent Systems and Computing, 755–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4367-2_72.

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Tong, Li, Haiwei Liang, and Xudong Zou. "Distribution Grid Topology Estimation Using 1D-CNN." In Lecture Notes in Electrical Engineering, 607–18. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1922-0_51.

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Ghosh, Sourodip, Aunkit Chaki, and Ankit Kudeshia. "Cyberbully Detection Using 1D-CNN and LSTM." In Lecture Notes in Electrical Engineering, 295–301. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4866-0_37.

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Baghel, Shikha, Mrinmoy Bhattacharjee, S. R. M. Prasanna, and Prithwijit Guha. "Shouted and Normal Speech Classification Using 1D CNN." In Lecture Notes in Computer Science, 472–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34872-4_52.

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Ji, Luping, Xiaorong Pu, and Guisong Liu. "Chinese Text Similarity Computation via the 1D-PW CNN." In Proceedings in Adaptation, Learning and Optimization, 237–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13359-1_19.

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Lang, Christian, Florian Steinborn, Oliver Steffens, and Elmar W. Lang. "Applying a 1D-CNN Network to Electricity Load Forecasting." In Contributions to Statistics, 205–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56219-9_14.

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Shivhare, Neha, Shanti Rathod, and M. R. Khan. "Dementia Detection Using Bi-LSTM and 1D CNN Model." In Algorithms for Intelligent Systems, 407–21. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9650-3_32.

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Gogoi, Parismita, Sishir Kalita, Wendy Lalhminghlui, Priyankoo Sarmah, and S. R. M. Prasanna. "Learning Mizo Tones from F0 Contours Using 1D-CNN." In Speech and Computer, 214–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87802-3_20.

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Liu, Xinyu, Gaole Sai, and Shengyu Duan. "Hardware Acceleration for 1D-CNN Based Real-Time Edge Computing." In Lecture Notes in Computer Science, 192–204. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21395-3_18.

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Beer Singh, Youddha, and Shivani Goel. "1D CNN based approach for speech emotion recognition using MFCC features." In Artificial Intelligence and Speech Technology, 347–54. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003150664-38.

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Conference papers on the topic "1D CNN"

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Allamy, Safaa, and Alessandro Lameiras Koerich. "1D CNN Architectures for Music Genre Classification." In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9659979.

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Matsunaga, Yuto, Naofumi Aoki, Yoshinori Dobashi, and Tetsuya Kojima. "Distortion based Watermark Extraction Technique Using 1D CNN." In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2021. http://dx.doi.org/10.1109/icaiic51459.2021.9415200.

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Jose, Avin, S. Ullas, and B. Uma Maheswari. "Collusion Detection in Electricity Markets Using 1D CNN." In 2022 International Conference on Intelligent Technologies (CONIT). IEEE, 2022. http://dx.doi.org/10.1109/conit55038.2022.9847862.

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Jouny, Ismail I. "Stepped frequency radar target recognition using 1D-CNN." In Automatic Target Recognition XXXII, edited by Kristen Jaskie, Timothy L. Overman, Riad I. Hammoud, and Abhijit Mahalanobis. SPIE, 2022. http://dx.doi.org/10.1117/12.2618613.

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Song, Yang, Zhifei Zhang, Razieh Kaviani Baghbaderani, Fanqi Wang, Ying Qu, Craig Stuttsy, and Hairong Qi. "Land Cover Classification for Satellite Images Through 1D CNN." In 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2019. http://dx.doi.org/10.1109/whispers.2019.8921180.

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XU, Yanping, Xia ZHANG, Tingcong YE, Zhenliang QIU, Lingjun ZHANG, Hua ZHANG, and Yifan WU. "1D CNN for Feature Reconstruction on Network Threat Detection." In ICMLC 2021: 2021 13th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3457682.3457701.

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Khan, Arshiya, and Chase Cotton. "Detecting Attacks on IoT Devices using Featureless 1D-CNN." In 2021 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2021. http://dx.doi.org/10.1109/csr51186.2021.9527910.

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Li, Yulan, Charlesetta Baidoo, Ting Cai, and Goodlet A. Kusi. "Speech Emotion Recognition Using 1D CNN with No Attention." In 2019 23rd International Computer Science and Engineering Conference (ICSEC). IEEE, 2019. http://dx.doi.org/10.1109/icsec47112.2019.8974716.

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Ileri, Ramis, Fatma Latifoglu, and Esra Demirci. "New Method to Diagnosis of Dyslexia Using 1D-CNN." In 2020 Medical Technologies Congress (TIPTEKNO). IEEE, 2020. http://dx.doi.org/10.1109/tiptekno50054.2020.9299241.

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Liu, Pengda, Julong Pan, Hailiang Zhu, and Yanli Li. "A Wearable Fall Detection System Based on 1D CNN." In 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE). IEEE, 2021. http://dx.doi.org/10.1109/icaice54393.2021.00046.

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Reports on the topic "1D CNN"

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Bozza, F., A. Gimelli, S. Fontanesi, and E. Severi. 1D and 3D CFD Investigation of Burning Process and Knock Occurrence in a Gasoline or CNG fuelled Two-Stroke SI Engine. Warrendale, PA: SAE International, November 2011. http://dx.doi.org/10.4271/2011-32-0526.

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Glushko, E. Ya, and A. N. Stepanyuk. New perspectives to improve accuracy of the molar gas constant using pneumatic photonic structures. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2873.

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In this work, a method is proposed to determine the molar constant R with the relative standard uncertainty near 10^-10 that is based on an extra accurate volume controlling and high sensitive pressure measurements in the framework of scale echeloning procedure. An essential moment of the method is uniting of results for two measurement scales with increased relative standard uncertainty (10^-5) to obtain the higher precise level. The gas-filled 1D elastic pneumatic photonic crystal is proposed as an optical indicator of pressure which can unite several pressure scales of magnitude.
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Nelson, Alex, Stanford A. Gibson, and Alex Sanchez. Development of a two-dimensional HEC-RAS sediment model for the Chippewa River, Wisconsin, for software development and sediment trend analysis. U.S. Army Engineer Research and Development Center, June 2022. http://dx.doi.org/10.21079/11681/44561.

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This US Army Corps of Engineers (USACE) Regional Sediment Management technical note (RSM-TN) describes an RSM effort that converted a one-dimensional (1D) sediment transport model of the Chippewa River confluence with the Mississippi River into a two-dimensional (2D) model. This work leveraged recent sediment data collection and tested the new 2D sediment transport capabilities in the Hydrologic Engineering Center, River Analysis System (HEC-RAS) Version 6.0. In addition to the benefits of software testing, the resulting model developed through this effort can provide more accurate spatial and temporal information about sedimentation in the Mississippi River navigation channel and help inform future dredging strategies for the St. Paul District, USACE.
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Glushko, E. Ya, and A. N. Stepanyuk. Optopneumatic medium for precise indication of pressure over time inside the fluid flow. Астропринт, 2018. http://dx.doi.org/10.31812/123456789/2874.

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In this work, a gas-filled 1D elastic pneumatic photonic crystal is proposed as an optical indicator of pressure which can unite several pressure scales of magnitude. The indicator includes layered elastic platform, optical fibers and switching valves, all enclosed into a chamber. We have investigated the pneumatic photonic crystal bandgap structure and light reflection changes under external pressure. At the chosen parameters the device may cover the pressure interval (0, 10) bar with extremely high accuracy (1 μbar) for actual pressures existing inside the biofluid systems of biological organisms. The size of the indicator is close to 1 mm and may be decreased. The miniaturized optical devices considered may offer an opportunity to organize simultaneous and total scanning monitoring of biofluid pressure in different parts of the circulatory systems.
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