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Using a one-dimensional convolutional neural network on FTIR spectroscopy to measure the thickness of composite plastic films

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INFRARED PHYSICS & TECHNOLOGY 2025年 147卷

作者： Wang, Xiaodong Ni, Liwei Zhang, Cheng Xu, Qiyue Ye, Shuliang China Jiliang Univ Inst Thermal Anal Technol & Instrumentat 258 Xueyuan St Hangzhou 310018 Peoples R China

Multilayer composite films have gained widespread application across a variety of industries and applications due to their unique properties and functionalities. The precise control of the thickness of each layer ensures that every layer contributes optimally to the desired attributes. In this study, a method using a one-dimensional convolutional neural network (1D-CNN) was developed to analyze the spectral data, five kinds of plastic films were assembled with different thickness combinations and measured with FTIR, which provided the data sets for 1D-CNN modeling. Compared to partial least squares (PLS) and fully connected neural network (FCNN) models, the 1D-CNN model performs better in accuracy of the thickness prediction, with respective root mean square error (RMSE) values dropping from < 9.11 mu m to < 0.31 mu m for film thickness of 13-270 mu m. The developed regression model was visualized using the gradient weighted class activation mapping (Grad-CAM) method for detailed analysis and selection of significant wavenumbers in order to build a more compact 1D-CNN model. Altogether, these results show that the proposed spectral 1D-CNN technique can measure the thickness of components in composite plastic films rapidly and accurately, and has the potential to improve efficiency and reduce the cost of instruments and calculations for spectral quantitation applications.

关键词： Composite plastic film Infrared spectrum One-dimensional convolutional neural network neural network visualization

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Seismic impedance inversion based on cycle-consistent generative adversarial network

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Petroleum Science 2022年第1期19卷 147-161页

作者： Yu-Qing Wang Qi Wang Wen-Kai Lu Qiang Ge Xin-Fei Yan The Institute for Artificial Intelligence Tsinghua University(THUAI)Beijing100084China State Key Laboratory of Intelligent Technology and Systems Tsinghua UniversityBeijing100084China Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijing100084China The Department of Automation Tsinghua UniversityBeijing100084China The Research Institute of Petroleum Exploration and Development China National Petroleum Corporation(CNPC)Beijing100083China

Deep learning has achieved great success in a variety of research fields and industrial ***,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based *** order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative *** kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training *** from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real ***,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN *** robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most *** the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.

关键词： Seismic inversion Cycle GAN Deep learning Semi-supervised learning neural network visualization

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Deep Insights into Convolutional networks for Video Recognition

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2020年第2期128卷 420-437页

作者： Feichtenhofer, Christoph Pinz, Axel Wildes, Richard P. Zisserman, Andrew Graz Univ Technol Graz Austria York Univ Toronto ON Canada Univ Oxford Oxford England

As the success of deep models has led to their deployment in all areas of computer vision, it is increasingly important to understand how these representations work and what they are capturing. In this paper, we shed light on deep spatiotemporal representations by visualizing the internal representation of models that have been trained to recognize actions in video. We visualize multiple two-stream architectures to show that local detectors for appearance and motion objects arise to form distributed representations for recognizing human actions. Key observations include the following. First, cross-stream fusion enables the learning of true spatiotemporal features rather than simply separate appearance and motion features. Second, the networks can learn local representations that are highly class specific, but also generic representations that can serve a range of classes. Third, throughout the hierarchy of the network, features become more abstract and show increasing invariance to aspects of the data that are unimportant to desired distinctions (e.g. motion patterns across various speeds). Fourth, visualizations can be used not only to shed light on learned representations, but also to reveal idiosyncrasies of training data and to explain failure cases of the system.

关键词： Computer vision Machine learning Deep learning Video recognition neural network visualization Action recognition

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An optimized convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods

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PATTERN RECOGNITION LETTERS 2018年 105卷 50-58页

作者： Jahani Heravi, Elnaz Habibi Aghdam, Hamed Puig, Domenec Univ Rovira & Virgili Dept Comp Engn & Math Tarragona Spain

Keeping record of daily meal intake is an effective solution for tackling with obesity and overweight. This can be done by developing apps on smartphones that are able to automatically recommend a short list of most probable foods by analyzing the photo taken from food. Then, the user chooses the correct answer from the short list. Hence, the automatic food recognition system must be able to recommend an accurate list. In other words, it is not essential for these apps to have a very high top-1 accuracy. Considering that the app will show the list of 5 most probable foods, the food recognition system must have a high top-5 accuracy. A food recognition system is usually developed by adapting knowledge of state-of-the-art networks such as GoogleNet and ResNet to the domain of food. However, these networks have high number of parameters. In this paper, we propose a 23-layer architecture which has 99.14% and 96.63% fewer parameter compared with ResNet and GoogleNet. Our experiment on Food101 and UECFood-256 datasets shows that although our network reduces the number of parameters dramatically, it produces more accurate results than GoogleNet and its accuracy is comparable with ResNet. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Food classification Convolutional neural networks neural network visualization Deep learning Spatial pyramid pooling

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A task-and-technique centered survey on visual analytics for deep learning model engineering

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COMPUTERS & GRAPHICS-UK 2018年第Dec.期77卷 30-49页

作者： Garcia, Rafael Telea, Alexandru C. da Silva, Bruno Castro Torresen, Jim Dihl Comba, Joao Luiz Univ Fed Rio Grande do Sul Porto Alegre RS Brazil Univ Oslo Oslo Norway Univ Groningen Groningen Netherlands

Although deep neural networks have achieved state-of-the-art performance in several artificial intelligence applications in the past decade, they are still hard to understand. In particular, the features learned by deep networks when determining whether a given input belongs to a specific class are only implicitly described concerning a considerable number of internal model parameters. This makes it harder to construct interpretable hypotheses of what the network is learning and how it is learning both of which are essential when designing and improving a deep model to tackle a particular learning task. This challenge can be addressed by the use of visualization tools that allow machine learning experts to explore which components of a network are learning useful features for a pattern recognition task, and also to identify characteristics of the network that can be changed to improve its performance. We present a review of modern approaches aiming to use visual analytics and information visualization techniques to understand, interpret, and fine-tune deep learning models. For this, we propose a taxonomy of such approaches based on whether they provide tools for visualizing a network's architecture, to facilitate the interpretation and analysis of the training process, or to allow for feature understanding. Next, we detail how these approaches tackle the tasks above for three common deep architectures: deep feedforward networks, convolutional neural networks, and recurrent neural networks. Additionally, we discuss the challenges faced by each network architecture and outline promising topics for future research in visualization techniques for deep learning models. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Visual analytics neural network visualization Deep learning visualization Deep learning neural networks Visual analytics survey

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High-Impedance Fault Detection Methodology Using Time-Frequency Spectrum and Transfer Convolutional neural network in Distribution networks

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IEEE SYSTEMS JOURNAL 2023年第3期17卷 4002-4013页

作者： Guo, Mou-Fa Guo, Zi-Yi Gao, Jian-Hong Chen, Duan-Yu Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Yuan Ze Univ Dept Elect Engn Taoyuan 32003 Taiwan

High-impedance fault (HIF) detection has always been difficult in distribution networks due to the lack of field data and the large difference between field and simulation waveforms. Based on the characteristics of zero-sequence currents, a novel HIF detection methodology is proposed, which combines time-frequency spectrum (TFS) and transfer convolutional neural network (TCNN). First, the TFSs are acquired by applying continuous wavelet transform (CWT) to the collected zero-sequence currents. Then, the TFSs of simulated zero-sequence currents are utilized for training source-domain convolutional neural network (SCNN). Next, the SCNN is transfer learned with very few TFSs of field zero-sequence currents to obtain TCNN. The performance of the proposed method is verified by simulation samples and field samples. The results show that the proposed method can effectively extract fault features from small-scale training samples under different fault circumstances. Besides, TCNN can adaptively extract the effective features of field HIF and detect field HIF more accurately than SCNN. Finally, this article provides a visualization scheme for interpretability of the neural network, which offers visual explanations for the decision-making basis of the neural network.

关键词： Distribution networks high-impedance fault (HIF) neural network visualization transfer convolutional neural network (TCNN) transfer learning

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Detection of skin cancer by classification of Raman spectra

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2004年第10期51卷 1784-1793页

作者： Sigurdsson, S Philipsen, PA Hansen, LK Larsen, J Gniadecka, M Wulf, HC Tech Univ Denmark DK-2800 Lyngby Denmark Univ Copenhagen Dept Pathol Bispebjerg Hosp DK-2400 Copenhagen Denmark

Skin lesion classification based on in vitro Raman spectroscopy is approached using a nonlinear neural network classifier. The classification framework is probabilistic and highly automated. The framework includes a feature extraction for Raman spectra and a fully adaptive and robust feedforward neural network classifier. Moreover, classification rules learned by the neural network may be extracted and evaluated for reproducibility, making it possible to explain the class assignment. The classification performance for the present data set, involving 222 cases and five lesion types, was 80.5% +/- 5.3% correct classification of malignant melanoma, which is similar to that of trained dermatologists based on visual inspection. The skin cancer basal cell carcinoma has a classification rate of 95.8% +/- 2.7%, which is excellent. The overall classification rate of skin lesions is 94.8% +/- 3.0%. Spectral regions, which are important for network classification, are demonstrated to reproduce. Small distinctive bands in the spectrum, corresponding to specific lipids and proteins, are shown to hold the discriminating information which the network uses to diagnose skin lesions.

关键词： neural network classifier neural network visualization pattern recognition skin cancer detection

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neural network training fingerprint: visual analytics of the training process in classification neural networks

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JOURNAL OF visualization 2022年第3期25卷 593-612页

作者： Ferreira, Martha Dais Cantareira, Gabriel D. de Mello, Rodrigo F. Paulovich, Fernando V. King's Coll London Dept Informatics London England Univ Sao Paulo Sao Carlos Brazil Dalhousie Univ Fac Comp Sci Halifax NS Canada Kings Coll London Dept Informat London England Univ Sao Paulo Sao Carlos Brazil

The striking results of deep neural networks (DNN) have motivated its wide acceptance to tackle large datasets and complex tasks such as natural language processing, facial recognition, and artificial image generation. However, DNN parameters are often empirically selected on a trial-and-error approach without detailed information on convergence behavior. While some visualization techniques have been proposed to aid the comprehension of general-purpose neural networks, only a few explore the training process, lacking the ability to adequately display how abstract representations are formed and represent the influence of training parameters during this process. This paper describes neural network training fingerprint (NNTF), a visual analytics approach to investigate the training process of any neural network performing classification. NNTF allows understanding how classification decisions change along the training process, displaying information about convergence, oscillations, and training rates. We show its usefulness through case studies and demonstrate how it can support the analysis of training parameters.

关键词： neural network visualization neural network training Deep learning Visual analytics visualization

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Deep Reinforcement Learning in Serious Games: Analysis and Design of Deep neural network Architectures 16th

Deep Reinforcement Learning in Serious Games: Analysis and D...

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16th International Conference on Computer Aided Systems Theory (EUROCAST)

作者： Dobrovsky, Aline Wilczak, Cezary W. Hahn, Paul Hofmann, Marko Borghoff, Uwe M. Univ Bundeswehr Munchen Fak Informat D-85577 Neubiberg Germany

ISBN: (纸本)9783319747279;9783319747262

Serious games present a noteworthy research area for artificial intelligence, where automated adaptation and reasonable NPC behaviour present essential challenges. Deep reinforcement learning has already been successfully applied to game-playing. We aim to expand and improve the application of deep learning methods in SGs through investigating their architectural properties and respective application scenarios. In this paper, we examine promising architectures and conduct first experiments concerning CNN design and analysis for game-playing. Although precise statements about the applicability of different architectures are not yet possible, our findings allow for concluding some general recommendations for the choice of DL architectures in different scenarios. Furthermore, we point out promising prospects for further research.

关键词： Deep learning Serious games Convolutional neural networks neural network visualization

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visualization AND INTERPRETATION OF SIAMESE STYLE CONVOLUTIONAL neural networkS FOR SOUND SEARCH BY VOCAL IMITATION

VISUALIZATION AND INTERPRETATION OF SIAMESE STYLE CONVOLUTIO...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhang, Yichi Duan, Zhiyao Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA

ISBN: (纸本)9781538646588

Designing systems that allow users to search sounds through vocal imitation augments the current text-based search engines and advances human-computer interaction. Previously we proposed a Siamese style convolutional network called IMINET for sound search by vocal imitation, which jointly addresses feature extraction by Convolutional neural network (CNN) and similarity calculation by Fully Connected network (FCN), and is currently the state of the art. However, how such architecture works is still a mystery. In this paper, we try to answer this question. First, we visualize the input patterns that maximize the activation of different neurons in each CNN tower;this helps us understand what features are extracted from vocal imitations and sound candidates. Second, we visualize the imitation-sound input pairs that maximize the activation of different neurons in the FCN layers;this helps us understand what kind of input pattern pairs are recognized during the similarity calculation. Interesting patterns are found to reveal the local-to-global and simple-to-conceptual learning mechanism of TL-IMINET. Experiments also show how transfer learning helps to improve TL-IMINET performance from the visualization aspect.

关键词： neural network visualization CNN Siamese style networks vocal imitation transfer learning

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