Cross-Modality Automatic Face Model Training from Large Video Databases

2005 ◽  
Author(s):  
Xiaodan Song ◽  
Ching-Yung Lin ◽  
Ming-Ting Sun
Keyword(s):  
Video Databases ◽  
Face Model ◽  
Model Training

3D Novel Face Model Based on Surface Stitching

2011 ◽  
Vol 135-136 ◽  
pp. 3-9
Author(s):  
Yun Ge ◽  
Yan Feng Sun ◽  
Bao Cai Yin ◽  
Heng Liang Tang
Keyword(s):  
Algorithm Design ◽  
Global Model ◽  
Training Algorithm ◽  
Important Data ◽  
Face Model ◽  
3D Face ◽  
Model Based ◽  
Data Platform ◽  
Morphable Model ◽  
Model Training

3D face sample is an important data platform for model training, algorithm design. Subject to the constraint of data acquisition equipment the size of current 3D face databases are relatively small and insufficient. To solve this problem, this paper presents a modeling way for generating 3D novel samples based on surface stitching. First we use morphable model to build a global model. Then, we replace each patch of the global model based on surface stitching. We demonstrate that with appropriate choice of local models it is possible to reliably generate new realistic face samples.

Privacy-preserving Collaborative Training for Medical Image Analysis Based on Multi-Blockchain

2020 ◽  
Vol 23 ◽  
Author(s):  
Wanlu Zhang ◽  
Qigang Wang ◽  
Mei Li
Keyword(s):  
Medical Image ◽  
Data Privacy ◽  
Medical Image Analysis ◽  
Auxiliary Information ◽  
Training Process ◽  
Private Data ◽  
Medical Institutions ◽  
Model Training ◽  
Collaborative Training ◽  
Similar Task

Background: As artificial intelligence and big data analysis develop rapidly, data privacy, especially patient medical data privacy, is getting more and more attention. Objective: To strengthen the protection of private data while ensuring the model training process, this article introduces a multi-Blockchain-based decentralized collaborative machine learning training method for medical image analysis. In this way, researchers from different medical institutions are able to collaborate to train models without exchanging sensitive patient data. Method: Partial parameter update method is applied to prevent indirect privacy leakage during model propagation. With the peer-to-peer communication in the multi-Blockchain system, a machine learning task can leverage auxiliary information from another similar task in another Blockchain. In addition, after the collaborative training process, personalized models of different medical institutions will be trained. Results: The experimental results show that our method achieves similar performance with the centralized model-training method by collecting data sets of all participants and prevents private data leakage at the same time. Transferring auxiliary information from similar task on another Blockchain has also been proven to effectively accelerate model convergence and improve model accuracy, especially in the scenario of absence of data. Personalization training process further improves model performance. Conclusion: Our approach can effectively help researchers from different organizations to achieve collaborative training without disclosing their private data.

scholarly journals Spatial Prediction of Future Flood Risk: An Approach to the Effects of Climate Change

Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 25
Author(s):  
Mohammadtaghi Avand ◽  
Hamid Reza Moradi ◽  
Mehdi Ramazanzadeh Lasboyee
Keyword(s):  
Climate Change ◽  
Spatial Prediction ◽  
Flood Management ◽  
Management Program ◽  
Climate Change Scenarios ◽  
Ann Model ◽  
Probability Map ◽  
Flood Susceptibility ◽  
Flood Probability ◽  
Model Training

Preparation of a flood probability map serves as the first step in a flood management program. This research develops a probability flood map for floods resulting from climate change in the future. Two models of Flexible Discrimination Analysis (FDA) and Artificial Neural Network (ANN) were used. Two optimistic (RCP2.6) and pessimistic (RCP8.5) climate change scenarios were considered for mapping future rainfall. Moreover, to produce probability flood occurrence maps, 263 locations of past flood events were used as dependent variables. The number of 13 factors conditioning floods was taken as independent variables in modeling. Of the total 263 flood locations, 80% (210 locations) and 20% (53 locations) were considered model training and validation. The Receiver Operating Characteristic (ROC) curve and other statistical criteria were used to validate the models. Based on assessments of the validated models, FDA, with a ROC-AUC = 0.918, standard error (SE = 0.038), and an accuracy of 0.86% compared to the ANN model with a ROC-AUC = 0.897, has the highest accuracy in preparing the flood probability map in the study area. The modeling results also showed that the factors of distance from the River, altitude, slope, and rainfall have the greatest impact on floods in the study area. Both models’ future flood susceptibility maps showed that the highest area is related to the very low class. The lowest area is related to the high class.

scholarly journals Detail Reconstruction of 3D Face Model from Single Image

2021 ◽  
Vol 1948 (1) ◽  
pp. 012053
Author(s):  
Hongxin Xu ◽  
Ruoming Lan ◽  
Tianping Li
Keyword(s):  
Single Image ◽  
Face Model ◽  
3D Face

Development of a machine learning algorithm for fault detection in a cantilever beam

2021 ◽  
pp. 095745652110004
Author(s):  
Amit Kumar Gorai ◽  
Tarapada Roy ◽  
Sumeet Mishra
Keyword(s):  
Mechanical Properties ◽  
Fault Detection ◽  
Cantilever Beam ◽  
Learning Algorithm ◽  
Model Development ◽  
Vibration Signal ◽  
Beam Specimen ◽  
Discrete Wavelet ◽  
Feed Forward Network ◽  
Model Training

The mechanical properties of a component change with any type of damage such as crack development, generation of holes, bend, excessive wear, and tear. The change in mechanical properties causes the material to behave differently in terms of noise and vibration under different loading conditions. Thus, the present study aims to develop an artificial neural network model using vibration signal data for early fault detection in a cantilever beam. The discrete wavelet transform coefficients of de-noised vibration signals were used for model development. The vibration signal was recorded using the OROS OR35 module for different fault conditions (no fault, notch fault, and hole fault) of a cantilever beam. A feed-forward network was trained using backpropagation to map the input features to output. A total of 603 training datasets (201 datasets for three types of cantilever beam—no fault, notch fault, and hole fault) were used for training, and 201 datasets were used for testing of the model. The testing dataset was recorded for a hole fault cantilever beam specimen. The results indicated that the proposed model predicted the test samples with 78.6% accuracy. To increase the accuracy of prediction, more data need to be used in the model training.

scholarly journals Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3162
Author(s):  
Nikolaos Kolokas ◽  
Dimosthenis Ioannidis ◽  
Dimitrios Tzovaras
Keyword(s):  
Optimization Algorithm ◽  
Energy Demand ◽  
Aggregate Demand ◽  
Multiple Model ◽  
Peak Shaving ◽  
Global Improvement ◽  
Forecasting Models ◽  
Consumption Cost ◽  
Model Training ◽  
Time Point

Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns.

scholarly journals Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

2021 ◽  
Vol 11 (2) ◽  
pp. 813
Author(s):  
Shuai Teng ◽  
Zongchao Liu ◽  
Gongfa Chen ◽  
Li Cheng
Keyword(s):  
Feature Extraction ◽  
Crack Detection ◽  
Computational Cost ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Computational Time ◽  
Image Size ◽  
Important Indicator ◽  
Feature Extractor ◽  
Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

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