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

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.

Download Full-text

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

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001408006442 ◽

2008 ◽

Vol 22 (04) ◽

pp. 765-781 ◽

Cited By ~ 1

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.

Download Full-text

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

10.26686/wgtn.14676087.v1 ◽

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.

Download Full-text

Low-Power Non-Binary LDPC Decoder Design via Adaptive Message Length Control Exploiting Domain-Specific Information

Journal of Signal Processing Systems ◽

10.1007/s11265-020-01533-2 ◽

2020 ◽

Vol 93 (1) ◽

pp. 11-23 ◽

Cited By ~ 1

Author(s):

Wenjie Huang ◽

Lei Wang

Keyword(s):

Low Power ◽

Specific Information ◽

Ldpc Decoder ◽

Domain Specific ◽

Message Length ◽

Decoder Design

Download Full-text

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

Information ◽

10.3390/info8020059 ◽

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

Download Full-text

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

10.26686/wgtn.14676087 ◽

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.

Download Full-text

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

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153816 ◽

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.

Download Full-text