Recognition unit model training based on competing word and word string models

1997 ◽  
Vol 102 (3) ◽  
pp. 1284
Author(s):  
Wu Chou
Keyword(s):  
Unit Model ◽  
Model Training ◽  
String Models ◽  
Word String

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.

A Method for Estimating Mass Transfer Coefficients in a Packed Column Using Reactive Absorption Data

2005 ◽  
Vol 70 (3) ◽  
pp. 383-402
Author(s):  
Valery A. Danilov ◽  
Il Moon
Keyword(s):  
Experimental Data ◽  
Mass Transfer ◽  
Optimization Technique ◽  
Packed Column ◽  
Effective Area ◽  
Transfer Coefficients ◽  
Mass Transfer Coefficients ◽  
Model Calculations ◽  
Unit Model ◽  
Reactive Absorption

This paper is devoted to the development of a new method for estimating mass transfer coefficients and effective area in packed columns in the case of reactive absorption. The method is based on a plug-flow model of reactive absorption of carbon dioxide with sodium hydroxide solution. The parameter estimation problem is solved using an optimization technique. Some mass transfer parameters are found to be correlated. Global sensitivity analysis by Sobol's technique showed that the unit model with the defined objective function is sensitive to the estimated parameter. Case studies of reactive absorption with different packings illustrate application of the proposed method for estimating mass transfer coefficients and effective area from column operation data. The model calculations are compared with experimental data obtained by other authors. The concentration profiles calculated by the unit model with the estimated parameters are shown to match well with experimental profiles from literature. A good agreement between estimated values and experimental data from literature confirms the applicability of this method.

scholarly journals Exploring the landscape of CHL strings on Td

2021 ◽  
Vol 2021 (8) ◽  
Author(s):  
Anamaría Font ◽  
Bernardo Fraiman ◽  
Mariana Graña ◽  
Carmen A. Núñez ◽  
Héctor Parra De Freitas
Keyword(s):  
Gauge Group ◽  
Moduli Space ◽  
Dynkin Diagram ◽  
Fundamental Groups ◽  
Computer Algorithms ◽  
Abelian Gauge ◽  
Reduced Rank ◽  
Systematic Exploration ◽  
String Models ◽  
Simply Connected

Abstract Compactifications of the heterotic string on special Td/ℤ2 orbifolds realize a landscape of string models with 16 supercharges and a gauge group on the left-moving sector of reduced rank d + 8. The momenta of untwisted and twisted states span a lattice known as the Mikhailov lattice II(d), which is not self-dual for d > 1. By using computer algorithms which exploit the properties of lattice embeddings, we perform a systematic exploration of the moduli space for d ≤ 2, and give a list of maximally enhanced points where the U(1)d+8 enhances to a rank d + 8 non-Abelian gauge group. For d = 1, these groups are simply-laced and simply-connected, and in fact can be obtained from the Dynkin diagram of E10. For d = 2 there are also symplectic and doubly-connected groups. For the latter we find the precise form of their fundamental groups from embeddings of lattices into the dual of II(2). Our results easily generalize to d > 2.

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.

IoT-based Epidemic Monitoring via Improved Gated Recurrent Unit Model

2021 ◽  
pp. 1-1
Author(s):  
Fang Hu ◽  
Jia Liu ◽  
Liuhuan Li ◽  
Mingfang Huang ◽  
Changguo Yang
Keyword(s):  
Epidemic Monitoring ◽  
Unit Model ◽  
Gated Recurrent Unit

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.

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