In this paper, we present an enhanced convolutional model for indoor radio map generation, focusing on the integration of a novel ray-marching feature. We describe our machinelearning pipeline developed for the ICASS...
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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 train...
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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 nondestruc...
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Battery pack layout is of great significance to enhance the thermal behavior of autonomous underwater vehicles(AUVs). Because battery pack layout is a high-dimensional and nonlinear problem, there is few research on t...
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Battery pack layout is of great significance to enhance the thermal behavior of autonomous underwater vehicles(AUVs). Because battery pack layout is a high-dimensional and nonlinear problem, there is few research on this topic at present. In order to more accurately predict the maximum temperature (MT) and temperature difference (TD) for different battery pack layouts, two machinelearning surrogate models were proposed in this paper, including support vector machine (SVM) and the feed-forward fully-connected neural network (FFN). Tens of thousands of battery pack layout scheme databases were obtained through the finite element method. Then, the machinelearning based methods were used to predict the MT and TD of the battery pack. The simulation results of this paper showed that both FFN and SVM have low mean absolute percentage error (MAPE) and mean square error (MSE), which means FFN and SVM can accurately predict the temperature. Meanwhile, it can be found that SVM has more advantage in small-scale problem. The methods in this paper can provide guidance for temperature prediction of AUV's battery pack layout.
Studies towards botnet activity detection methods have become a major focus over the years since many concerns have been raised due to botnets. The detection against Domain Name System (DNS) queries, which bots widely...
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In recent years, the industry has sought insights from abundant data generated by drilling operations. One of the key focus areas is the rate of penetration (ROP) which impacts costs directly, and emissions indirectly...
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ISBN:
(纸本)9780791885956
In recent years, the industry has sought insights from abundant data generated by drilling operations. One of the key focus areas is the rate of penetration (ROP) which impacts costs directly, and emissions indirectly. Previous work has succeeded in predicting and optimizing ROP, however was limited to specific fields and small-scale applications. This limitation stems from unobserved information between different fields or operations that often impacts model usability. This paper provides a new way of well planning by leveraging the power of unsupervised machinelearning to deliver higher drilling efficiency, lower costs, and less uncertainty. Unsupervised machinelearning techniques are used to infer information about drilling operations from real-time data that is not directly measured. Certain well types seem to be separable in low dimensions, based on qualitative interpretation of clusters visualized in 2D, and according to analyzing cluster membership based on which wells the data came from, and by associating common characteristics of wells within clusters using external information. This work introduces a novel approach to collect, separate, and extract value from data that is otherwise unused. data synthesized by Variational Autoencoders could be used for enhanced well planning, sold as a standalone product, or shared between industry players with reduced privacy concerns, increasing the wealth of data available.
Recently there is an emergent curiosity among researchers to apply machinelearning algorithms over diversified real world complications to get simpler *** notion behind this briefing is to represent the basic machine...
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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...
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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
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 ...
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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 machinelearning and deep learning models, proposed method reduces the need of manual data denoising and feature extraction.
machinelearning as an emerging and applicable learning plays a vital role in the world of Computer Science. machinelearning has attracted much attention in the last decade. With the development of machinelearning, h...
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