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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33935 条记录，以下是991-1000 订阅

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Damage evaluation of concrete beams using forced vibration testing and machine learning 11th

Damage evaluation of concrete beams using forced vibration t...

引用

11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Fujisaku, Yusuke Naito, Hideki Japan, Sendai Inaba, Kohko Department of Civil and Environmental Engineering Graduate School of Engineering Tohoku University Japan Railway Technical Research Institute Tokyo Japan

ISBN: (纸本)9781032345314

A decrease in skilled infrastructure inspectors and the cost of maintenance are big issues in Japan. Thus, an effective, automated inspection system is much needed. In this study, local vibration testing, a nondestructive testing method, coupled with machine learning, was utilized. Local vibration testing has the benefit of detecting damage that exists in deep layers because the waves travel through-thickness of concrete members. Using a portable vibrator, local vibration testing was conducted on reinforced concrete (RC) beams to detect the location of damages within. The RC beam specimens had two types of damages: flexural cracks and shear cracks. Results showed that the damaged conditions' frequency responses varied from that of the intact condition. Additionally, high abnormality degrees were detected around the crack region. This implies that the location of damages can be identified from the data produced by local vibration testing. Though this method alone yields results, introducing automation would further increase the diagnostic efficiency. Therefore, machine learning was introduced. A non-supervised learning method was applied. Using this method, the damages were identified with a very high accuracy rate. This indicates that the combination of local vibration testing and non-supervised learning can produce an effective, automated inspection system for the integrity diagnosis of RC beams. © 2022 the Author(s).

关键词： Frequency response

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An Overview of Text Representation Techniques in Text Classification using Deep learning Models 3

An Overview of Text Representation Techniques in Text Classi...

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Patil, Darshan Lokare, Reena Patil, Sunita University of Mumbai K. J. Somaiya Institute of Engineering and Information Technology Mumbai India

ISBN: (纸本)9781665494991

Organizations handling huge amount of data needs to preserve privacy of the documents. Every customer has the rights to ask for privacy of their documents. These documents can be classified into different categories like private, public, confidential, etc. Using suitable text classification methods, documents can be classified into different categories. Various approaches like machine learning (ML), deep learning (DL), natural language processing (NLP) is available. machine learning algorithms are showing acceptable performance but doesn't work well when data grows in size. In this paper, convolutional neural network(CNN) which is a deep learning model, is used to classify documents into different categories. Deep learning models are beneficial over machine learning models in terms of performance and volume of data to be classified. The performance of the model is evaluated and obtained acceptable performance. © 2022 IEEE.

关键词： Deep learning

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Temporal Analysis and Forecasting of Rainfall of Kerala: A machine learning Based Approach 2

Temporal Analysis and Forecasting of Rainfall of Kerala: A M...

引用

2nd international conference in Civil engineering for a Sustainable Planet, ICCESP 2024

作者： George, Stalin Oommen, Riya Elza Radhakrishnan, T. Kerala Thiruvananthapuram India

ISBN: (数字)9780784486047

ISBN: (纸本)9780784486047

In agrarian societies, rainfall variability significantly affects water availability, impacting both drought and flood risks. This study explores the implications of rainfall variations in Kerala, using CHIRPS data and Google Earth Engine. Rainfall patterns from 2000 to 2021 were analyzed to establish a baseline, and data from 2022 revealed deviations from the average. Despite Kerala’s high average rainfall of 3,269 mm in 2022, months like January, February, October, and November showed negative deviations, suggesting potential meteorological drought conditions. The Standardized Precipitation Index (SPI) identified drought-like conditions in districts such as Wayanad, Palakkad, Idukki, and Thiruvananthapuram, while districts like Kottayam, Ernakulam, Alappuzha, and Kannur experienced above-normal rainfall, raising flood concerns. A random forest regressor model, trained on long-term data, was employed to predict rainfall patterns. The model's predictions, validated against CHIRPS data, achieved a 95% accuracy score, demonstrating its effectiveness in forecasting rainfall trends. The methodology developed can be applied to other regions and timeframes. © ASCE.

关键词： Drought

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Deep Residual SVM: A Hybrid learning Approach to obtain High Discriminative Feature for Land Use and Land Cover Classification

Deep Residual SVM: A Hybrid Learning Approach to obtain High...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Kumari, Neha Minz, Sonajharia School of Computer and Systems Sciences JNU New Delhi India

Classification of multispectral images is impacted by challenges such as inadequate training samples, limited ground truth, and complex spatiotemporal dependencies. The accuracy of classifiers due to the lack of training samples is also compounded by the class imbalance problem in spatial analytics. This paper proposes a novel idea of exploiting the deep residual networks trained on millions of images for high discriminative feature representations. These derived representations are classified using bias-corrected Adam optimizer-based Support Vector machine classifiers. The intuition behind this approach is the significance of representations in the last layers of the pre-trained network that contributes to the classification accuracy. These encapsulated representations from the pre-trained network can outperform traditional feature representations. Residual networks solved the vanishing gradients problem in deep learning and improved classification accuracy. Hence, this work explores the combined potential of the representation power of deep residual pre-trained networks and the classification ability of Support Vector machines. The model implementation experiments have been conducted using two publicly available benchmark datasets, Eurosat and UC Merced for Land Use and Land Cover Classification. The proposed model DRSVM (Deep Residual Support Vector machines) demonstrated higher efficiency in terms of computational parameters and time complexity than conventional convolutional neural networks, pre-trained networks, and Support Vector machines with comparable accuracy. data augmentation techniques are used further for enhancing the performance of the model. Conventional supervised deep learning algorithms like CNN overfit in dense layers in case of inadequate training data. This hybrid approach alleviates overfitting issues by eliminating dense layers and replacing the Softmax function with Support Vector machines for classification. © 2022 Elsevier B.V.. All righ

关键词： Support vector machines

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Power system anomaly recognition based on distinction enhancement of positive anomaly data

Power system anomaly recognition based on distinction enhanc...

引用

2024 international conference on Smart Transportation and City engineering, STCE 2024

作者： Luo, Chuan Zhao, Yangjie Liu, Ziqi Chen, Long School of Computer Science China West Normal University Nanchong China Education and Information Technology Center China West Normal University Nanchong China China Tobacco Sichuan Industrial Co. Ltd. Chengdu China School of Electronic Information Engineering China West Normal University Nanchong China

ISBN: (纸本)9781510690585

After power system enters the era of Internet of Things, false data injection attack poses a serious threat to the stability of power system. The false data injection attack, crafted from the grid's topology and physical model, closely resembles real data. This similarity causes traditional machine learning models to struggle with high false alarm rates and low accuracy in detecting the false data. For this reason, this paper proposes a power system anomaly recognition method based on the differentiated enhancement of positive anomaly data (PADE-AR). The method utilizes Autoencoder (AE) to learn the intrinsic representation of normal data, reconstructs the normal characteristics of the data, then amplifies the difference between normal and false data by calculating the difference of the corresponding features between the original data and the reconstructed data by AE, to improve the accuracy of false data detection and reduce the false alarm rate. On the Mississippi State University and Oak Ridge National Laboratory dataset and on the three-phase circuit dataset, experiments show that compared with traditional machine learning models, PADE-AR achieved improvement in the accuracy of false data detection and has good wide applicability. © 2025 SPIE.

关键词： Anomaly detection

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Prediction of Disc Cutter Replacement of Tunnel Boring machine Using Denoising Auto Encoder 31st

Prediction of Disc Cutter Replacement of Tunnel Boring Machi...

引用

31st international conference on Flexible Automation and Intelligent Manufacturing

作者： Liu, Yang Huang, Shuaiwen Wang, Di Zhu, Guoli Zhang, Dailin Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9783031176289;9783031176296

While a tunnel boring machine (TBM) is working, rocks are crushed into pieces by disc cutters which often fail during construction. To replace disabled cutters timely, the condition of the cutters needs to be checked regularly. However, this is time-consuming and uneconomical. In this paper, a denoising auto encoder (DAE) model is proposed to judge whether TBM disc cutters need to be replaced. First of all, the field data related to dis cutter status are selected as inputs. Then, the cutter conditions can be learned automatically base on a DAE model. Test result on a water transport tunnel shows that the proposed model can obtain an average accuracy of 99.7% and an average F1 score of 99.4% on field data prediction. Compared with other machine learning and deep learning models, proposed method reduces the need of manual data denoising and feature extraction.

关键词： Tunnel boring machine Disc cutter Deep learning Denoising auto encoder

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SINO-CT-FUSION-NET: A LIGHTWEIGHT DEEP learning FRAMEWORK FOR DETECTION AND CLASSIFICATION OF INTRACRANIAL HEMORRHAGES 31

SINO-CT-FUSION-NET: A LIGHTWEIGHT DEEP LEARNING FRAMEWORK FO...

引用

2024 international conference on Image Processing

作者： Sindhura, Chitimireddy Yalavarthy, Phaneendra K. Gorthi, Subrahmanyam Indian Inst Technol Tirupati Dept Elect Engn Chindepalle Andhra Pradesh India Indian Inst Sci Dept Computat & Data Sci Bengaluru India

ISBN: (纸本)9798350349405;9798350349399

Deep learning has significantly improved medical diagnostics with its ability to learn the underlying complex patterns. A sinogram contains a sequence of X-ray projections of the patient into a lower dimensional space from different viewing angles, and a CT image is obtained as a result of applying reconstruction algorithms on the sinograms acquired by the scanner. While CT images are commonly used for automated diagnosis, recent developments have demonstrated that sinogram-based approaches can provide results on par with CT-based methods. This work leverages from the advantages of both approaches through the fusion of features learned from both those images. This paper presents a new lightweight deep learning model to detect and classify Intracranial Hemorrhages (ICH) through the fusion of high-level features learned from both sinogram and CT images. The proposed method is trained and evaluated on the publicly available RSNA ICH dataset. Furthermore, we analyze its multi-label classification capability in categorizing hemorrhages into five types. The proposed fusion model outperformed both CT-based and sinogram-based methods in general, it is particularly useful when there is less annotated training data and limited computational resources. The code and data can be found at https://***/sindhura234/SinoCTFusionNet

关键词： CT Sinogram Deep learning machine learning Computer Aided Diagnosis Intracranial Hemorrhage

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Research On machine Analogy Translation Technology Based On machine learning 2

Research On Machine Analogy Translation Technology Based On ...

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2nd international conference on Digital Society and Intelligent Systems, DSInS 2022

作者： Zhuang, Siyu Cai, Yingyi South China Normal University No. 55 West of Yat-sen Avenue Guangdong Province China

ISBN: (纸本)9781510663244

With the acceleration of economic globalization, the importance of translation services has gradually manifested. The machine translation technology has a long and glorious history from statistical machine translation to neural machine translation, At present, machine translation software is emerging, mostly following the rules of natural language processing. In order to achieve quality and efficiency in translation, the various ambiguities in the language should be processed accurately and the speed of translation should be increased, while the anthropomorphism of machine translation should be enhanced and the machine's knowledge of human language should be constantly improved. Therefore, it is important to strengthen the research of machine learning technology, and to insist on the research on machine analogy translation technology based on machine learning is an important research topic. © 2023 SPIE.

关键词： Computer aided language translation

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Optimization Analysis and Standardization of Rolling Process with machine learning

Optimization Analysis and Standardization of Rolling Process...

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2022 IEEE international conference on Big data, Big data 2022

作者： Cho, Seongwoo Jung, Yujin Seo, Yongdeuk Jung, Kyuheon Choi, Sungchul Pukyong National University School of Systems Management and Safety Engineering Busan Korea Republic of Pukyong National University Department of Industrial and Data Engineering Busan Korea Republic of

ISBN: (纸本)9781665480451

This study proposes a model based on machine learning(ML) for predicting the thickness of a coil in a steel rolling process. To achieve this objective, it is necessary to establish an engineering strategy based on data collected from actual rolling sites and to develop multiple models. Five models are trained on preprocessed process data, and the optimal model is determined through experiments. © 2022 IEEE.

关键词： machine learning

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Automatic Generation of Visualizations for machine learning Pipelines 7

Automatic Generation of Visualizations for Machine Learning ...

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37th IEEE/ACM international conference on Automated Software engineering (ASE)

作者： Liu, Lei Chen, Wei-Peng Bahrami, Mehdi Prasad, Mukul Fujitsu Res Amer Inc Sunnyvale CA 94085 USA

ISBN: (纸本)9781450394758

Visualization is very important for machine learning (ML) pipelines because it can showexplorations of the data to inspire data scientists and show explanations of the pipeline to improve understandability. In this paper, we present a novel approach that automatically generates visualizations for ML pipelines by learning visualizations from highly-upvoted Kaggle pipelines. The solution extracts both code and dataset features from these high-quality human-written pipelines and corresponding training datasets, learns the mapping rules from code and dataset features to visualizations using association rule mining (ARM), and finally uses the learned rules to predict visualizations for unseen ML pipelines. The evaluation results show that the proposed solution is feasible and effective to generate visualizations for ML pipelines.

关键词： Pipelines

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