Large Volume Metrology Process Model: Measurability Analysis with Integration of Metrology Classification Model and Feature-Based Selection Model

2010 ◽  
pp. 1013-1026 ◽  
Author(s):  
Chun Hung Cheng ◽  
Dehong Huo ◽  
Xi Zhang ◽  
Wei Dai ◽  
Paul G. Maropoulos
Keyword(s):  
Large Volume ◽  
Process Model ◽  
Selection Model ◽  
Classification Model ◽  
Feature Based ◽  
Measurability Analysis

scholarly journals A Variational Point Process Model for Social Event Sequences

2020 ◽  
Vol 34 (01) ◽  
pp. 173-180
Author(s):  
Zhen Pan ◽  
Zhenya Huang ◽  
Defu Lian ◽  
Enhong Chen
Keyword(s):  
Point Process ◽  
Real World ◽  
Process Model ◽  
Classification Model ◽  
Event Sequence ◽  
Event Sequences ◽  
Sequence Modeling ◽  
Stochastic Point Process ◽  
Feature Based ◽  
Real World Datasets

Many events occur in real-world and social networks. Events are related to the past and there are patterns in the evolution of event sequences. Understanding the patterns can help us better predict the type and arriving time of the next event. In the literature, both feature-based approaches and generative approaches are utilized to model the event sequence. Feature-based approaches extract a variety of features, and train a regression or classification model to make a prediction. Yet, their performance is dependent on the experience-based feature exaction. Generative approaches usually assume the evolution of events follow a stochastic point process (e.g., Poisson process or its complexer variants). However, the true distribution of events is never known and the performance depends on the design of stochastic process in practice. To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. The model is termed Variational Event Point Process (VEPP). Our model introduces variational auto-encoder to event sequence modeling that can better use the latent information and capture the distribution over inter-arrival time and types of event sequences. Experiments on real-world datasets prove effectiveness of our proposed model.

Statistical Time Domain Feature Based Approach to Assess the Performance Degradation of Rotary Seals

2018 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique
Keyword(s):  
Time Domain ◽  
Oil And Gas ◽  
High Reliability ◽  
Accelerated Aging ◽  
Oil And Gas Industry ◽  
Oil Field ◽  
Classification Model ◽  
Severity Estimation ◽  
Feature Based ◽  
Statistical Time

In oil and gas industry, machineries and mechanical components are designed with high reliability to meet the demand of the oil field. Rotating machinery is a widely used equipment and any failure of critical components within the machinery could lead to delays and large expenses. Failure of rotary seal is one of the foremost causes of breakdown in rotary machinery and such a failure can affect the other process operations in oil and gas plants. Assessing seal degradation and severity estimation are very important for maintenance decision-making. Extracting meaningful and sensitive features that can show seal degradation from raw signals is a challenging task of degradation assessment. However, no extensive works are dedicated in this area of seals. In this paper, we perform accelerated aging and testing to capture the behavior of seals through their cycle of operation and demonstrated a statistical time domain feature based approach for extracting the sensitive features that can show seal degradation. Out of eleven statistical features extracted, seven extracted features such as mean, RMS, maximum, squared mean rooted absolute amplitude, impulse factor, crest factor, margin factor are found to be significant factors which have a potential to differentiate severity levels in seals. The findings from our work show that our approach has a potential to assess the severity in seals. As a possible extension, extracted features can be used to build a classification model to classify severity in seals which could be of great interest to the users and manufacturers of rotary seals.

scholarly journals A Multi-Task Learning Approach for Answer Selection: A Study and a Chinese Law Dataset

2019 ◽  
Vol 33 ◽  
pp. 9935-9936
Author(s):  
Wenyu Du ◽  
Baocheng Li ◽  
Min Yang ◽  
Qiang Qu ◽  
Ying Shen
Keyword(s):  
Question Answering ◽  
Selection Model ◽  
Classification Model ◽  
Learning Approach ◽  
Selection System ◽  
Document Representation ◽  
High Quality ◽  
Chinese Law ◽  
Task Learning ◽  
Multiple Domains

In this paper, we propose a Multi-Task learning approach for Answer Selection (MTAS), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains (tasks). Specifically, MTAS consists of two key components: (i) A category classification model that learns rich category-aware document representation; (ii) An answer selection model that provides the matching scores of question-answer pairs. These two tasks work on a shared document encoding layer, and they cooperate to learn a high-quality answer selection system. In addition, a multi-head attention mechanism is proposed to learn important information from different representation subspaces at different positions. We manually annotate the first Chinese question answering dataset in law domain (denoted as LawQA) to evaluate the effectiveness of our model. The experimental results show that our model MTAS consistently outperforms the compared methods.1

scholarly journals The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haijing Tang ◽  
Taoyi Wang ◽  
Mengke Li ◽  
Xu Yang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Fetal Heart ◽  
Current Medical Research ◽  
Classification Model ◽  
Classification Method ◽  
Portable Devices ◽  
Feature Based ◽  
Feature Acquisition ◽  
Time Diagnosis

Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named “MKNet” and recurrent neural network named “MKRNN” are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.

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