- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Accurate prediction model of bead geometry in crimping butt of the laser brazing using generalized regression neural network
摘要： There are few researches that concentrate on the prediction of the bead geometry for laser brazing with crimping butt. This paper addressed the accurate prediction of the bead profile by developing a generalized regression neural network (GRNN) algorithm. Firstly GRNN model was developed and trained to decrease the prediction error that may be influenced by the sample size. Then the prediction accuracy was demonstrated by comparing with other articles and back propagation artificial neural network (BPNN) algorithm. Eventually the reliability and stability of GRNN model were discussed from the points of average relative error (ARE), mean square error (MSE) and root mean square error (RMSE), while the maximum ARE and MSE were 6.94% and 0.0303 that were clearly less than those (14.28% and 0.0832) predicted by BPNN. Obviously, it was proved that the prediction accuracy was improved at least 2 times, and the stability was also increased much more.
关键词： bead geometry,generalized regression neural network,prediction model,laser brazing
更新于2025-09-11 14:15:04
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[IEEE 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD) - Istanbul, Turkey (2019.3.21-2019.3.24)] 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD) - Photovoltaic power forecasting through temperature and solar radiation estimation
摘要： The Utilization of the photovoltaic power as a source of electricity has been strongly growing. The unpredictability of the PV power energy induces frequency fluctuations and power system instabilities. Thus, short term PV power prediction, from one hour to several hours, becomes very important to ensure grid stability. The photovoltaic power depends on different weather conditions mostly temperature and solar radiation. Therefore, weather data forecasting becomes highly recommended. This paper presents a comparison study between the adaptive neuro-fuzzy inference system and the feed forward neural network for one hour ahead temperature and solar radiation estimation using different input data. Two and four hours ahead forecasting of the metrological data are done using the feed forward neural network model. Using the forecasted weather data, the photovoltaic power is deduced. The accuracy of the topologies is based on the normalized root mean square error (NRMSE), and the mean absolute percentage error (MAPE) The simulation results show that the FFNN outperforms the ANFIS model.
关键词： photovoltaic power,FFNN,ANFIS,prediction,solar radiation,temperature
更新于2025-09-11 14:15:04
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Deep Visual Saliency on Stereoscopic Images
摘要： Visual saliency on stereoscopic 3D (S3D) images has been shown to be heavily influenced by image quality. Hence, this dependency is an important factor in image quality prediction, image restoration and discomfort reduction, but it is still very difficult to predict such a nonlinear relation in images. In addition, most algorithms specialized in detecting visual saliency on pristine images may unsurprisingly fail when facing distorted images. In this paper, we investigate a deep learning scheme named Deep Visual Saliency (DeepVS) to achieve a more accurate and reliable saliency predictor even in the presence of distortions. Since visual saliency is influenced by low-level features (contrast, luminance and depth information) from a psychophysical point of view, we propose seven low-level features derived from S3D image pairs and utilize them in the context of deep learning to detect visual attention adaptively to human perception. During analysis, it turns out that the low-level features play a role to extract distortion and saliency information. To construct saliency predictors, we weight and model the human visual saliency through two different network architectures, a regression and a fully convolutional neural networks (CNNs). Our results from thorough experiments confirm that the predicted saliency maps are up to 70 % correlated with human gaze patterns, which emphasize the need for the hand-crafted features as input to deep neural networks in S3D saliency detection.
关键词： deep learning,convolutional neural network,stereoscopic image,Saliency prediction,distorted image
更新于2025-09-11 14:15:04
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Fast Non-Linear Methods for Dynamic Texture Prediction
摘要： This paper aims to develop a fast dynamic-texture prediction method, using tools from non-linear dynamical modeling, and fast approaches for approximate regression. We consider dynamic textures to be described by patch-level non-linear processes, thus requiring tools such as delay-embedding to uncover a phase-space where dynamical evolution can be more easily modeled. After mapping the observed time-series from a dynamic texture video to its recovered phase-space, a time-efficient approximate prediction method is presented which utilizes locality-sensitive hashing approaches to predict possible phase-space vectors, given the current phase-space vector. Our experiments show the favorable performance of the proposed approach, both in terms of prediction fidelity, and computational time. The proposed algorithm is applied to shading prediction in utility scale solar arrays.
关键词： solar energy,shading prediction,dynamic textures,phase-space reconstruction
更新于2025-09-10 09:29:36
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Macro-Pixel Prediction Based on Convolutional Neural Networks for Lossless Compression of Light Field Images
摘要： The paper introduces a novel macro-pixel prediction method based on Convolutional Neural Networks (CNN) for lossless compression of light field images. In the proposed method, each macro-pixel is predicted based on a volume of macro-pixels from its immediate causal neighborhood. The proposed deep neural network operates on these macro-pixel volumes and provides accurate macro-pixel prediction in light field images. The resulting macro-pixel residuals are encoded by a reference codec built based on the CALIC codec. A context modeling method for light field images is proposed. Experimental results on a large light field image dataset show that the proposed prediction method systematically and substantially outperforms state-of-the-art predictors. To our knowledge, the paper is the first to introduce deep-learning based prediction of macro-pixels, enabling efficient lossless compression of light field images.
关键词： CNN-based prediction,Intraprediction,lossless compression,macro-pixel,light field images
更新于2025-09-10 09:29:36
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - A Proactive Restoration Strategy for Optical Cloud Networks Based on Failure Predictions
摘要： Failure prediction based on the anomaly detection/forecasting is becoming a reality thanks to the introduction of machine learning techniques. The orchestration layer can leverage on this new feature to proactively reconfigure cloud services that might find themselves traversing an element that is about to fail. As a result, the number of cloud service interruptions can be reduced with beneficial effects in terms of cloud service availability. Based on the above intuition, this paper presents an orchestration strategy for optical cloud networks able to reconfigure vulnerable cloud services (i.e., the ones that would be disrupted if a predicted failure happens) before an actual failure takes place. Simulation results demonstrate that, with a single link failure scenario, proactive restoration can lead to up to 97% less cloud services having to be relocated. This result brings considerable benefits in terms of cloud service availability, especially in low load conditions. It is also shown that these improvements come with almost no increase in the cloud service blocking probability performance, i.e., resource efficiency is not impacted.
关键词： Cloud services,Software defined networking (SDN),Availability,Failure prediction,Orchestration,Cloud service relocation,Resiliency,Proactive recovery,Restoration
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Object Detection with Head Direction in Remote Sensing Images Based on Rotational Region CNN
摘要： Object detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. In this paper, we propose a novel detection framework based on rotational region convolution neural network to cope with the problem of non-maximum suppression in dense objects detection. The bounding boxes obtained by adopting our method is the minimum bounding rectangle of object with less redundant regions. Furthermore, we find the head direction of the object through prediction. There are three important changes to our framework over traditional detection methods, representation and regression of rotational bounding box, head direction prediction and rotational non-maximal suppression. Experiments based on remote sensing images from Google Earth for Object detection show that our detection method based on rotational region CNN has a competitive performance.
关键词： prediction,object detection,rotating region,convolution neural network,non-maximal suppression
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - The Development of a High Precision Troposphere Effect Mitigation Processor for Sar Interferometry
摘要： Troposphere effect mitigation based on numerical weather prediction (NWP) is an actual research topic in SAR interferometry (InSAR) and especially in persistent scatterer interferometry (PSI). This is the reason, a scientific troposphere effect mitigation processing system has been developed. The objective of this paper is to provide the methodology of four developed algorithms, demonstrate application examples, discuss the methods characteristic and recommend techniques for operational systems.
关键词： wide area processing (WAP),numerical weather prediction (NWP),atmospheric phase screen (APS),persistent scatterer interferometry (PSI)
更新于2025-09-10 09:29:36
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Development and validation of an algorithm to predict the treatment modality of burn wounds using thermographic scans: Prospective cohort study
摘要： Background The clinical evaluation of a burn wound alone may not be adequate to predict the severity of the injury nor to guide clinical decision making. Infrared thermography provides information about soft tissue viability and has previously been used to assess burn depth. The objective of this study was to determine if temperature differences in burns assessed by infrared thermography could be used predict the treatment modality of either healing by re-epithelization, requiring skin grafts, or requiring amputations, and to validate the clinical predication algorithm in an independent cohort. Methods and findings Temperature difference (ΔT) between injured and healthy skin were recorded within the first three days after injury in previously healthy burn patients. After discharge, the treatment modality was categorized as re-epithelization, skin graft or amputation. Potential confounding factors were assessed through multiple linear regression models, and a prediction algorithm based on the ΔT was developed using a predictive model using a recursive partitioning Random Forest machine learning algorithm. Finally, the prediction accuracy of the algorithm was compared in the development cohort and an independent validation cohort. Significant differences were found in the ΔT between treatment modality groups. The developed algorithm correctly predicts into which treatment category the patient will fall with 85.35% accuracy. Agreement between predicted and actual treatment for both cohorts was weighted kappa 90%. Conclusion Infrared thermograms obtained at first contact with a wounded patient can be used to accurately predict the definitive treatment modality for burn patients. This method can be used to rationalize treatment and streamline early wound closure.
关键词： treatment modality,prediction algorithm,burn wounds,Random Forest machine learning,infrared thermography
更新于2025-09-10 09:29:36
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[ASME ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - Quebec City, Quebec, Canada (Sunday 26 August 2018)] Volume 1B: 38th Computers and Information in Engineering Conference - Predicting Manufactured Shapes of a Projection Micro-Stereolithography Process via Convolutional Encoder-Decoder Networks
摘要： Projection micro-stereolithography (P-μSLA) processes have been widely utilized in three-dimensional (3D) digital fabrication. However, various uncertainties of a photopolymerization process often deteriorates the geometric accuracy of fabrication results. A predictive model that maps input shapes to actual outcomes in real-time would be immensely beneficial for designers and process engineers, permitting rapid design exploration through inexpensive trials-and-errors, such that optimal design parameters as well as optimal shape modification plan could be identified with only minimal waste of time, material, and labor. However, no computational model has ever succeeded in predicting such geometric inaccuracies to a reasonable precision. In this regard, we propose a novel idea of predicting output shapes from input projection patterns of a P-μSLA process via deep neural networks. To this end, a convolutional encoder-decoder network is proposed in this paper. The network takes a projection image as the input and returns a predicted shape after fabrication as the output. Cross-validation analyses showed the root-mean-square-error (RMSE) of 10.72 μm in average, indicating noticeable performance of the proposed convolutional encoder-decoder network.
关键词： P-μSLA,convolutional encoder-decoder network,Projection micro-stereolithography,shape deformation prediction,deep neural networks
更新于2025-09-10 09:29:36