scholarly journals A Two-Stage Joint Model for Domain-Specific Entity Detection and Linking Leveraging an Unlabeled Corpus

Information ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 59
Author(s):  
Hongzhi Zhang ◽  
Weili Zhang ◽  
Tinglei Huang ◽  
Xiao Liang ◽  
Kun Fu
Keyword(s):  
Joint Model ◽  
Two Stage ◽  
Domain Specific

Two-Stage Configurable Decoder Model for Domain Specific FEC Decoder Design

2011 ◽  
Vol E94-A (12) ◽  
pp. 2659-2668
Author(s):  
Ittetsu TANIGUCHI ◽  
Ayataka KOBAYASHI ◽  
Keishi SAKANUSHI ◽  
Yoshinori TAKEUCHI ◽  
Masaharu IMAI
Keyword(s):  
Two Stage ◽  
Domain Specific ◽  
Decoder Design

scholarly journals Optimization of Two-Stage IPD-(1+I) Controllers for Frequency Regulation of Sustainable Energy Based Hybrid Microgrid Network

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 919
Author(s):  
Abdul Latif ◽  
S. M. Suhail Hussain ◽  
Dulal Chandra Das ◽  
Taha Selim Ustun
Keyword(s):  
Sustainable Energy ◽  
Research Field ◽  
Optimization Techniques ◽  
Frequency Regulation ◽  
Water Heater ◽  
Two Stage ◽  
Domain Specific ◽  
Hybrid Microgrid ◽  
Main Challenge ◽  
Algorithmic Technique

Sustainable energy based hybrid microgrids are advantageous in meeting constantly increasing energy demands. Conversely, the intermittent nature of renewable sources represents the main challenge to achieving a reliable supply. Hence, load frequency regulation by adjusting the amount of power shared between subsystems is considered as a promising research field. Therefore, this paper presents a new stratagem for frequency regulation by developing a novel two stage integral-proportional-derivative with one plus integral (IPD-(1+I)) controller for multi sources islanded microgrid system (MS-IμGS). The proposed stratagem has been tested in an MS-IμGS comprising of a wind turbine, parabolic trough, biodiesel generators, solid-oxide fuel cell, and electric water heater. The proposed model under different scenarios is simulated in MATLAB environment considering the real-time recorded wind data. A recently developed sine-cosine algorithmic technique (SCA) has been leveraged for optimal regulation of frequency in the considered microgrid. To identify the supremacy of the proposed technique, comparative studies with other classical controllers with different optimization techniques have been performed. From the comparison, it is clearly evident that, SCA-(IPD-(1+I)) controller gives better performance over other considered stratagems in terms of various time domain specific parameters, such as peak deviations (overshoot, undershoot) and settling time. Finally, the robustness of the proposed stratagem is evaluated by conducting sensitivity analysis under ±30% parametric variations and +30% load demand. The lab tests results validate the operation of the proposed system and show that it can be used to regulate the frequency in stand-alone microgrids with a high penetration of renewable energy.

A FAST TWO-STAGE CONTENT-BASED IMAGE RETRIEVAL APPROACH IN THE DCT DOMAIN

2008 ◽  
Vol 22 (04) ◽  
pp. 765-781 ◽  
Author(s):  
TIENWEI TSAI ◽  
YO-PING HUANG ◽  
TE-WEI CHIANG
Keyword(s):  
Image Retrieval ◽  
High Efficiency ◽  
Feature Space ◽  
Content Based Image Retrieval ◽  
Two Stage ◽  
Feature Extraction Method ◽  
Domain Specific ◽  
Retrieval Scheme ◽  
Domain Specific Knowledge ◽  
Distinct Features

In this paper, a two-stage content-based image retrieval (CBIR) approach is proposed to improve the retrieval performance. To develop a general retrieval scheme which is less dependent on domain-specific knowledge, the discrete cosine transform (DCT) is employed as a feature extraction method. In establishing the database, the DC coefficients of Y, U and V components are quantized such that the feature space is partitioned into a finite number of grids, each of which is mapped to a grid code (GC). When querying an image, at coarse classification stage, the grid-based classification (GBC) and the distance threshold pruning (DTP) serve as a filter to remove those candidates with widely distinct features. At the fine classification stage, only the remaining candidates need to be computed for the detailed similarity comparison. The experimental results show that both high efficacy and high efficiency can be achieved simultaneously using the proposed two-stage approach.

scholarly journals An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

2021 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Xiaoying Gao ◽  
Yi Mei ◽  
S Ghosh ◽  
J Li
Keyword(s):  
Classification Problem ◽  
Substantial Improvement ◽  
Two Stage ◽  
Data Set ◽  
Unified Medical Language System ◽  
Domain Specific ◽  
Second Stage ◽  
Medical Text ◽  
Medical Language ◽  
Medical Documents

Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.

Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model

2020 ◽  
pp. 1471082X2094506
Author(s):  
Katya Mauff ◽  
Nicole S. Erler ◽  
Isabella Kardys ◽  
Dimitris Rizopoulos
Keyword(s):  
Importance Sampling ◽  
Estimation Procedure ◽  
Joint Model ◽  
Limited Information ◽  
Time To Event ◽  
Two Stage ◽  
Bivariate Model ◽  
Longitudinal Outcomes ◽  
Credible Intervals ◽  
Stage Estimation

Multiple longitudinal outcomes are theoretically easily modelled via extension of the generalized linear mixed effects model. However, due to computational limitations in high dimensions, in practice these models are applied only in situations with relatively few outcomes. We adapt the solution proposed by Fieuws and Verbeke (2006) to the Bayesian setting: fitting all pairwise bivariate models instead of a single multivariate model, and combining the Markov Chain Monte Carlo (MCMC) realizations obtained for each pairwise bivariate model for the relevant parameters. We explore importance sampling as a method to more closely approximate the correct multivariate posterior distribution. Simulation studies show satisfactory results in terms of bias, RMSE and coverage of the 95% credible intervals for multiple longitudinal outcomes, even in scenarios with more limited information and non-continuous outcomes, although the use of importance sampling is not successful. We further examine the incorporation of a time-to-event outcome, proposing the use of Bayesian pairwise estimation of a multivariate GLMM in an adaptation of the corrected two-stage estimation procedure for the joint model for multiple longitudinal outcomes and a time-to-event outcome ( Mauff et al., 2020 , Statistics and Computing). The method does not work as well in the case of the corrected two-stage joint model; however, the results are promising and should be explored further.

scholarly journals Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Behnaz Alafchi ◽  
Hossein Mahjub ◽  
Leili Tapak ◽  
Ghodratollah Roshanaei ◽  
Mohammad Ali Amirzargar
Keyword(s):  
Renal Transplantation ◽  
Joint Modeling ◽  
Joint Model ◽  
Single Event ◽  
Two Stage ◽  
Longitudinal Outcomes ◽  
Proposed Model ◽  
Longitudinal Biomarker ◽  
Multivariate Longitudinal Outcomes ◽  
Multistate Process

In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their association with the survival process is of interest. In the present study, we applied a joint model of various longitudinal biomarkers and transitions between different health statuses in patients who underwent renal transplantation. The full joint likelihood approaches are faced with the complexities in computation of the likelihood. So, here, we have proposed two-stage modeling of multivariate longitudinal outcomes and multistate conditions to avoid these complexities. The proposed model showed reliable results compared to the joint model in case of joint modeling of univariate longitudinal biomarker and the multistate process.

scholarly journals An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

2021 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Xiaoying Gao ◽  
Yi Mei ◽  
S Ghosh ◽  
J Li
Keyword(s):  
Classification Problem ◽  
Substantial Improvement ◽  
Two Stage ◽  
Data Set ◽  
Unified Medical Language System ◽  
Domain Specific ◽  
Second Stage ◽  
Medical Text ◽  
Medical Language ◽  
Medical Documents

Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.

Two-stage self-seeding method for preparation of single crystals of poly(phenylene sulfide)

1989 ◽  
Vol 47 ◽  
pp. 368-369
Author(s):  
Sengshiu Chung ◽  
Peggy Cebe
Keyword(s):  
Single Crystals ◽  
High Performance ◽  
Dilute Solution ◽  
Two Stage ◽  
Annealing Behavior ◽  
High Performance Polymers ◽  
Seeding Method ◽  
Phenylene Sulfide

We are studying the crystallization and annealing behavior of high performance polymers, like poly(p-pheny1ene sulfide) PPS, and poly-(etheretherketone), PEEK. Our purpose is to determine whether PPS, which is similar in many ways to PEEK, undergoes reorganization during annealing. In an effort to address the issue of reorganization, we are studying solution grown single crystals of PPS as model materials.Observation of solution grown PPS crystals has been reported. Even from dilute solution, embrionic spherulites and aggregates were formed. We observe that these morphologies result when solutions containing uncrystallized polymer are cooled. To obtain samples of uniform single crystals, we have used two-stage self seeding and solution replacement techniques.

364: Two stage "Progressive Elongation of Spermatic Cord" Technique in Criptorchidism with very short Spermatic Vessels

2007 ◽  
Vol 177 (4S) ◽  
pp. 121-121
Author(s):  
Antonio Dessanti ◽  
Diego Falchetti ◽  
Marco Iannuccelli ◽  
Susanna Milianti ◽  
Gian P. Strusi ◽  
...  
Keyword(s):  
Spermatic Cord ◽  
Two Stage
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