A new model combining building block displacement and building block area reduction for resolving spatial conflicts

2021 ◽  
Author(s):  
Parastoo Pilehforooshha ◽  
Mohammad Karimi ◽  
Ali Mansourian
Keyword(s):  
Building Block ◽  
New Model ◽  
Area Reduction ◽  
Model Combining ◽  
Block Area

scholarly journals Improved Hough Transform and Total Variation Algorithms for Features Extraction of Wood

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 466
Author(s):  
Weiwei Du ◽  
Yarui Xi ◽  
Kiichi Harada ◽  
Yumei Zhang ◽  
Keiko Nagashima ◽  
...  
Keyword(s):  
Total Variation ◽  
Hough Transform ◽  
Annual Ring ◽  
Wood Quality ◽  
Volume Density ◽  
Low Noise ◽  
Impact Factors ◽  
Annual Rings ◽  
New Model ◽  
Model Combining

Research shows that the intensity impact factors of wood, such as late timber ratio, volume density and the intensity of itself, correlate with the width of wood annual rings. Therefore, extracting wood annual ring information from wood images is helpful for evaluating wood quality. During the past few years, many researchers have conducted defect detection by studying the information of wood images. However, there are few in-depth studies on the statistics and calculation of wood annual ring information. This study proposes a new model combining the Total Variation (TV) algorithm and the improved Hough transform to accurately measure the wood annual ring information. The TV algorithm is used to suppress image noise, and the Hough transform is for detecting the center of the wood image. Moreover, the edges of wood annual rings are extracted, and the statistical ring information is calculated. The experimental results show that the new model has good denoising capability, clearly extract the edges of wood annual rings and calculate the related parameters from the indoor wood images of the processed logs and the unprocessed low-noise logs.

Sparse Associative Memory

2019 ◽  
Vol 31 (5) ◽  
pp. 998-1014 ◽  
Author(s):  
Heiko Hoffmann
Keyword(s):  
Associative Memory ◽  
Building Block ◽  
Basic Building Block ◽  
Small Probability ◽  
Hopfield Networks ◽  
New Model ◽  
Brain Functions ◽  
Memory Efficiency ◽  
Low Efficiency

It is still unknown how associative biological memories operate. Hopfield networks are popular models of associative memory, but they suffer from spurious memories and low efficiency. Here, we present a new model of an associative memory that overcomes these deficiencies. We call this model sparse associative memory (SAM) because it is based on sparse projections from neural patterns to pattern-specific neurons. These sparse projections have been shown to be sufficient to uniquely encode a neural pattern. Based on this principle, we investigate theoretically and in simulation our SAM model, which turns out to have high memory efficiency and a vanishingly small probability of spurious memories. This model may serve as a basic building block of brain functions involving associative memory.

A new model combining the liver/spleen volume ratio and classification of varices predicts HVPG in hepatitis B patients with cirrhosis

2015 ◽  
Vol 27 (3) ◽  
pp. 335-343 ◽  
Author(s):  
Shi-ping Yan ◽  
Hao Wu ◽  
Guang-chuan Wang ◽  
Yong Chen ◽  
Chun-qing Zhang ◽  
...  
Keyword(s):  
Hepatitis B ◽  
Volume Ratio ◽  
Spleen Volume ◽  
New Model ◽  
Model Combining

scholarly journals A new tropospheric tomography model combining the pixel-based and function-based models

2018 ◽  
Author(s):  
Yibin Yao ◽  
Linyang Xin ◽  
Qingzhi Zhao
Keyword(s):  
Water Vapor ◽  
Three Dimensional ◽  
Global Navigation Satellite Systems ◽  
Reference Station ◽  
Continuous Change ◽  
Vapor Density ◽  
Spatial Continuity ◽  
New Model ◽  
Model Combining ◽  
And Function

Abstract. As a new detection method of three-dimensional water vapor, the ground-based water vapor tomography technique using Global Navigation Satellite Systems (GNSS) observations can obtain the high spatial and temporal distribution information of tropospheric water vapor. Since the troposphere tomography was proposed, most previous studies belong to the pixel-based method, dividing the interest area into three-dimensional voxels of which the water vapor density of each voxel center is taken as the average water vapor density. However, the abovementioned method can only find the water vapor density value of the center of each voxel, which is unable to express the continuous change of water vapor in space and destroys the spatial continuity of water vapor variation. Moreover, when using the pixel-based method, too many voxels are needed to express the water vapor density, which leads to the problem of too many coefficients to be estimated. After analyzing the limitations of the traditional pixel-based troposphere tomography technique, this paper proposes a new GNSS tropospheric water vapor tomography model combining the pixel-based and function-based models for the first time. The tomographic experiences were validated using the data from 12 stations from the Hong Kong Satellite Positioning Reference Station Network (SatRef) collected between 25 March and 25 April 2014. The comparison between tomographic results and the European Centre for Medium-Range Weather Forecasts (ECMWF) data is mainly used to analyze the accuracy of the new model proposed in this paper under different conditions, for showing that this new model is superior to the traditional pixel-based model in terms of root-mean-square error (RMSE) and bias. The new model has more advantages than the traditional pixel-based model on the RMSE, especially when obtaining the water vapor in voxels without the penetration of GNSS rays, which is improved by 5.88 %. This model also solves the problem with more ease and convenience in expression.

scholarly journals Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy

2021 ◽  
Vol 8 ◽  
Author(s):  
Sae X. Morita ◽  
Kenya Kusunose ◽  
Akihiro Haga ◽  
Masataka Sata ◽  
Kohei Hasegawa ◽  
...  
Keyword(s):  
Deep Learning ◽  
Genetic Testing ◽  
Reference Model ◽  
New Model ◽  
Net Reclassification Improvement ◽  
Mayo Score ◽  
Model Combining ◽  
Echocardiographic Images ◽  
Sensitivity Specificity ◽  
Learning Analysis

Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models—the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the p-value by performing 1,000 bootstrapping. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we examined the net reclassification improvement. We included 99 adults with HCM who underwent genetic testing. Overall, 45 patients (45%) had positive genotype. The new model combining Mayo score and DCNN-derived probability significantly outperformed Mayo score (AUC 0.86 [95% CI 0.79–0.93] vs. 0.72 [0.61–0.82]; p < 0.001). Similarly, the new model combining Toronto score and DCNN-derived probability exhibited a higher AUC compared to Toronto score alone (AUC 0.84 [0.76–0.92] vs. 0.75 [0.65–0.85]; p = 0.03). An improvement in the sensitivity, specificity, PPV, and NPV was also achieved, along with significant net reclassification improvement. In conclusion, compared to the conventional models, our new model combining the conventional and DCNN-derived models demonstrated superior accuracy to predict positive genotype in patients with HCM.

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