3D seismic forward modeling from the multiphysical inversion at the Ketzin CO_(2) storage site

From June 2008 to August 2013,approximately 67 kt of CO_(2) was injected into a deep saline formation at the Ketzin pilot CO_(2) storage site.During injection,3D seismic surveys have been performed to monitor the migration of sequestered CO_(2).Seismic monitoring results are limited by the acquisition and signal-to-noise ratio of the acquired data.The multiphysical reservoir simulation provides information regarding the CO_(2) fluid behavior,and the approximated model should be calibrated with the monitoring results.In this work,property models are delivered from the multiphysical model during 3D repeated seismic surveys.The simulated seismic data based on the models are compared with the real data,and the results validate the effectiveness of the multiphysical inversion method.Time-lapse analysis shows the trend of CO_(2) migration during and after injection.

基于深度学习的人脸识别

随着目标的正确识别逐渐成为人工智能的重要组成部分,基于深度学习的人脸识别目前也成为了特征识别领域的研究热点。但是因为脸部信息的复杂性,对特征识别算法的要求会更高。笔者简述了人脸识别与深度学习的基本结构,并对人脸识别技术目前的应用情况与发展方向进行了深入分析。

Fault detection in flotation processes based on deep learning and support vector machine

Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.

A method for workpiece surface small-defect detection based on CutMix and YOLOv3

Surface small defects are often missed and incorrectly detected due to their small quantity and unapparent visual features.A method named CSYOLOv3,which is based on CutMix and YOLOv3,is proposed to solve such a problem.First,a four-image CutMix method is used to increase the small-defect quantity,and the process is dynamically adjusted based on the beta distribution.Then,the classic YOLOv3 is improved to detect small defects accurately.The shallow and large feature maps are split,and several of them are merged with the feature maps of the predicted branch to preserve the shallow features.The loss function of YOLOv3 is optimized and weighted to improve the attention to small defects.Finally,this method is used to detect 512×512 pixel images under RTX 2060Ti GPU,which can reach the speed of 14.09 frame/s,and the mAP is 71.80%,which is 5%-10%higher than that of other methods.For small defects below 64×64 pixels,the mAP of the method reaches 64.15%,which is 14%higher than that of YOLOv3-GIoU.The surface defects of the workpiece can be effectively detected by the proposed method,and the performance in detecting small defects is significantly improved.

Neighborhood fusion-based hierarchical parallel feature pyramid network for object detection

In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network(FPN)and deconvolutional single shot detector(DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4%to 5%higher mAP(mean average precision)than SSD,and 2%to 3%higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300×300 input reaches 79.4%mAP at 34.6 frame/s,and the mAP can raise to 82.9%after using the multi-scale testing strategy.

External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images

Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation.Methods The ULDor system consists of a convolutional neural network(CNN)trained on around 80 K lesion annotations from about 12 K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets.During the validation process,the test sets include two parts:the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital,and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial(NLST).We ran the model on the two test sets to output lesion detection.Three board-certified radiologists read the CT scans and verified the detection results of ULDor.We used positive predictive value(PPV)and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images,including liver,kidney,pancreas,adrenal,spleen,esophagus,thyroid,lymph nodes,body wall,thoracic spine,etc.Results In the external validation,the lesion-level PPV and sensitivity of the model were 57.9%and 67.0%,respectively.On average,the model detected 2.1 findings per set,and among them,0.9 were false positives.ULDor worked well for detecting liver lesions,with a PPV of 78.9%and a sensitivity of 92.7%,followed by kidney,with a PPV of 70.0%and a sensitivity of 58.3%.In internal validation with NLST test set,ULDor obtained a PPV of 75.3%and a sensitivity of 52.0%despite the relatively high noise level of soft tissue on images.Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs.With further optimisation and iterative upgrades,ULDor may be well suited for extensive application to external data.

Multidimensional attention and multiscale upsampling for semantic segmentation

Semantic segmentation is for pixel-level classification tasks,and contextual information has an important impact on the performance of segmentation.In order to capture richer contextual information,we adopt ResNet as the backbone network and designs an encoder-decoder architecture based on multidimensional attention(MDA)module and multiscale upsampling(MSU)module.The MDA module calculates the attention matrices of the three dimensions to capture the dependency of each position,and adaptively captures the image features.The MSU module adopts parallel branches to capture the multiscale features of the images,and multiscale feature aggregation can enhance contextual information.A series of experiments demonstrate the validity of the model on Cityscapes and Camvid datasets.

Prostate Cancer Risk Prediction and Online Calculation Based on Machine Learning Algorithm

Objective To build a prostate cancer(PCa) risk prediction model based on common clinical indicators to provide a theoretical basis for the diagnosis and treatment of PCa and to evaluate the value of artificial intelligence(AI) technology under healthcare data platforms.Methods After preprocessing of the data from Population Health Data Archive,smuothly clipped absolute deviation(SCAD) was used to select features.Random forest(RF),support vector machine(SVM),back propagation neural network(BP),and convolutional neural network(CNN) were used to predict the risk of PCa,among which BP and CNN were used on the enhanced data by SMOTE.The performances of models were compared using area under the curve(AUC) of the receiving operating characteristic curve.After the optimal model was selected,we used the Shiny to develop an online calculator for PCa risk prediction based on predictive indicators.Results Inorganic phosphorus,triglycerides,and calcium were closely related to PCa in addition to the volume of fragmented tissue and free prostate-specific antigen(PSA).Among the four models,RF had the best performance in predicting PCa(accuracy:96.80%;AUC:0.975,95% CI:0.964-0.986).Followed by BP(accuracy:85.36%;AUC:0.892,95% CI:0.849-0.934) and SVM(accuracy:82.67%;AUC:0.824,95% CI:0.805-0.844).CNN performed worse(accuracy:72.37%;AUC:0.724,95% CI:0.670-0.779).An online platform for PCa risk prediction was developed based on the RF model and the predictive indicators.Conclusions This study revealed the application value of traditional machine learning and deep learning models in disease risk prediction under healthcare data platform,proposed new ideas for PCa risk prediction in patients suspected for PCa and had undergone core needle biopsy.Besides,the online calculation may enhance the practicability of AI prediction technology and facilitate medical diagnosis.