This document proposes a generalized definition language for implementing an object-based fuzzy class model. It begins by reviewing related work on defining fuzzy classes and identifying limitations in existing approaches. It then summarizes the authors' previous work developing a generalized fuzzy class structure and model. The document introduces several new data types for representing different types of fuzzy attributes. Finally, it proposes a formal definition language for the fuzzy class model that utilizes the new data types to define fuzzy class structure and accurately represent fuzzy data types and attribute values. The language is intended to serve as a data definition language for object-based fuzzy database systems.
Suitability of naïve bayesian methods for paragraph level text classification...ijaia
The amount of data present online is growing very rapidly, hence a need for organizing and categorizing
data has become an obvious need. The Information Retrieval (IR) techniques act as an aid in assisting
users in obtaining relevant information. IR in the Indian context is very relevant as there are several blogs,
news publications in Indian languages present online. This work looks at the suitability of Naïve Bayesian
methods for paragraph level text classification in the Kannada language. The Naïve Bayesian methods are
the most primitive algorithms for Text Categorization tasks. We apply dimensionality reduction technique
using Minimum term frequency, stop word identification and elimination methods for achieving the task. It
is evident that Naïve Bayesian Multinomial model outperforms simple Naïve Bayesian approach in
paragraph classification tasks.
Using Decision Tree for Automatic Identification of Bengali Noun-Noun Compoundsidescitation
This paper presents a supervised machine learning
approach that uses a decision tree learning algorithm for
recognition of Bengali noun-noun compounds as multiword
expression (M WE) from Bengali corpus. Our proposed
approach to MWE recognition has two steps: (1) extraction of
candidate multi-word expressions using chunk information
and various heuristic rules and (2) training the machine
learning algorithm to recognize a candidate multi-word
expression as Multi-word expression or not. A variety of
association measures have been used as features for
identifying MWEs. The proposed system is tested on a Bengali
corpus for identifying noun-noun compound MWEs from the
corpus.
Suitability of naïve bayesian methods for paragraph level text classification...ijaia
The amount of data present online is growing very rapidly, hence a need for organizing and categorizing
data has become an obvious need. The Information Retrieval (IR) techniques act as an aid in assisting
users in obtaining relevant information. IR in the Indian context is very relevant as there are several blogs,
news publications in Indian languages present online. This work looks at the suitability of Naïve Bayesian
methods for paragraph level text classification in the Kannada language. The Naïve Bayesian methods are
the most primitive algorithms for Text Categorization tasks. We apply dimensionality reduction technique
using Minimum term frequency, stop word identification and elimination methods for achieving the task. It
is evident that Naïve Bayesian Multinomial model outperforms simple Naïve Bayesian approach in
paragraph classification tasks.
Using Decision Tree for Automatic Identification of Bengali Noun-Noun Compoundsidescitation
This paper presents a supervised machine learning
approach that uses a decision tree learning algorithm for
recognition of Bengali noun-noun compounds as multiword
expression (M WE) from Bengali corpus. Our proposed
approach to MWE recognition has two steps: (1) extraction of
candidate multi-word expressions using chunk information
and various heuristic rules and (2) training the machine
learning algorithm to recognize a candidate multi-word
expression as Multi-word expression or not. A variety of
association measures have been used as features for
identifying MWEs. The proposed system is tested on a Bengali
corpus for identifying noun-noun compound MWEs from the
corpus.
Farthest Neighbor Approach for Finding Initial Centroids in K- MeansWaqas Tariq
Text document clustering is gaining popularity in the knowledge discovery field for effectively navigating, browsing and organizing large amounts of textual information into a small number of meaningful clusters. Text mining is a semi-automated process of extracting knowledge from voluminous unstructured data. A widely studied data mining problem in the text domain is clustering. Clustering is an unsupervised learning method that aims to find groups of similar objects in the data with respect to some predefined criterion. In this work we propose a variant method for finding initial centroids. The initial centroids are chosen by using farthest neighbors. For the partitioning based clustering algorithms traditionally the initial centroids are chosen randomly but in the proposed method the initial centroids are chosen by using farthest neighbors. The accuracy of the clusters and efficiency of the partition based clustering algorithms depend on the initial centroids chosen. In the experiment, kmeans algorithm is applied and the initial centroids for kmeans are chosen by using farthest neighbors. Our experimental results shows the accuracy of the clusters and efficiency of the kmeans algorithm is improved compared to the traditional way of choosing initial centroids.
Centralized Class Specific Dictionary Learning for wearable sensors based phy...Sherin Mathews
With recent progress in pervasive healthcare,
physical activity recognition with wearable body sensors has
become an important and challenging area in both research and
industrial communities. Here, we address a novel technique for
a sensor platform that performs physical activity recognition by
leveraging a class specific regularizer term into the dictionary
pair learning objective function. The proposed algorithm jointly
learns a synthesis dictionary and an analysis dictionary in
order to simultaneously perform signal representation and
classification once the time-domain features have been extracted.
Specifically, the class specific regularizer term ensures that the
sparse codes belonging to the same class will be concentrated
thereby proving beneficial for the classification stage. In order
to develop a more practical approach, we employ a combination
of an alternating direction method of multipliers and a l1 − ls
minimization method to approximately minimize the objective
function. We validate the effectiveness of our proposed model
by employing it on two activity recognition problem and an
intensity estimation problem, both of which include a large
number of physical activities. Experimental results demonstrate
that classifiers built in this dictionary learning based framework
outperforms state of art algorithms by using simple features,
thereby achieving competitive results when compared with
classical systems built upon features with prior knowledge
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Guidelines for ER to Relational Mapping.
Mapping rules/ guidelines for mapping various ER constructs to Relational model with appropriate examples
Relational Query Languages Formal Query Languages
Introduction to Relational Algebra
Relational operators
Set operators
Join operators
Aggregate functions.
Grouping operator
Relational Calculus concepts
Relational algebra queries for data retrieval with sample relational schemas. relational algebra operations.
EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION O...cscpconf
Classification is a step by step practice for allocating a given piece of input into any of the given
category. Classification is an essential Machine Learning technique. There are many
classification problem occurs in different application areas and need to be solved. Different
types are classification algorithms like memory-based, tree-based, rule-based, etc are widely
used. This work studies the performance of different memory based classifiers for classification
of Multivariate data set from UCI machine learning repository using the open source machine
learning tool. A comparison of different memory based classifiers used and a practical
guideline for selecting the most suited algorithm for a classification is presented. Apart fromthat some empirical criteria for describing and evaluating the best classifiers are discussed
In this paper firstly I have compared Single Label Text Categorization with Multi Label Text Categorization in detail then I have compared Document Pivoted Categorization with Category Pivoted Categorization in detail. For this purpose I have given the general definition of Text Categorization with its mathematical notation for the purpose of its frugality and cost effectiveness. Then with the help of mathematical notation and set theory ,I have converted the general definitions of Single Label Text Categorization and Multi Label Text Categorization into their respective mathematical representation .Then I discussed Binary Text Categorization as a special case of Single Label Text Categorization. After comparison of Single Label Text Categorization with Multi Label Text Categorization, I found that Single Label Text Categorization or Binary Text Categorization is more general than Multi Label Text Categorization. Thereafter I discussed an algorithm for transformation of Multi Label Classification into Binary Classification and explained the conditions of transformation of Multi Label Classification into Binary Classification. In the second step I compared Document Pivoted Categorization with Category Pivoted Categorization in detail. After comparison we found that Category Pivoted Categorization is more typical and complex than Document Pivoted Categorization. The Category Pivoted Categorization becomes more complicated when new category is added to predefined set of categories and the recurrent classification of documents takes place. Finally I compared Hard Categorization with Ranking Categorization. After comparing them I found that Hard Categorization incorporates ‘Hard Decisions’ about the relevance or belonging of a document to a category. This hard decision is either completely true or completely false. Whereas the Ranking Categorization creates a belonging of a document to a category
according to the estimated appropriateness to the document. The final Ranked List is developed in the Ranking Categorization which is used by the human expert for final decision of Text Categorization.
Feature selection on boolean symbolic objectsijcsity
With the boom in IT technology, the data sets used in
application are more and more larger and are
described by a huge number of attributes, therefore, the feature selection become an important discipline in
Knowle
dge discovery and data mining, allowing the experts
to select the most relevant features to impr
ove
the quality of their studies and to reduce the time processing of their algorithm. In addition to that, the data
used by the applications become richer. They are now represented by a set of complex and structured
objects, instead of simple numerical ma
trixes. The purpose of
our
algorithm is to do feature selection on
rich data,
called Boolean Symbolic Objects
(BSOs)
. These objects are desc
ribed by multivalued features.
The
BSOs
are considered as higher level units which can model complex data, such as c
luster of
individuals, aggregated data or taxonomies. In this paper we will introduce a new feature selection
criterion for
BSOs
, and we will explain
how we improved its complexity.
Farthest Neighbor Approach for Finding Initial Centroids in K- MeansWaqas Tariq
Text document clustering is gaining popularity in the knowledge discovery field for effectively navigating, browsing and organizing large amounts of textual information into a small number of meaningful clusters. Text mining is a semi-automated process of extracting knowledge from voluminous unstructured data. A widely studied data mining problem in the text domain is clustering. Clustering is an unsupervised learning method that aims to find groups of similar objects in the data with respect to some predefined criterion. In this work we propose a variant method for finding initial centroids. The initial centroids are chosen by using farthest neighbors. For the partitioning based clustering algorithms traditionally the initial centroids are chosen randomly but in the proposed method the initial centroids are chosen by using farthest neighbors. The accuracy of the clusters and efficiency of the partition based clustering algorithms depend on the initial centroids chosen. In the experiment, kmeans algorithm is applied and the initial centroids for kmeans are chosen by using farthest neighbors. Our experimental results shows the accuracy of the clusters and efficiency of the kmeans algorithm is improved compared to the traditional way of choosing initial centroids.
Centralized Class Specific Dictionary Learning for wearable sensors based phy...Sherin Mathews
With recent progress in pervasive healthcare,
physical activity recognition with wearable body sensors has
become an important and challenging area in both research and
industrial communities. Here, we address a novel technique for
a sensor platform that performs physical activity recognition by
leveraging a class specific regularizer term into the dictionary
pair learning objective function. The proposed algorithm jointly
learns a synthesis dictionary and an analysis dictionary in
order to simultaneously perform signal representation and
classification once the time-domain features have been extracted.
Specifically, the class specific regularizer term ensures that the
sparse codes belonging to the same class will be concentrated
thereby proving beneficial for the classification stage. In order
to develop a more practical approach, we employ a combination
of an alternating direction method of multipliers and a l1 − ls
minimization method to approximately minimize the objective
function. We validate the effectiveness of our proposed model
by employing it on two activity recognition problem and an
intensity estimation problem, both of which include a large
number of physical activities. Experimental results demonstrate
that classifiers built in this dictionary learning based framework
outperforms state of art algorithms by using simple features,
thereby achieving competitive results when compared with
classical systems built upon features with prior knowledge
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Guidelines for ER to Relational Mapping.
Mapping rules/ guidelines for mapping various ER constructs to Relational model with appropriate examples
Relational Query Languages Formal Query Languages
Introduction to Relational Algebra
Relational operators
Set operators
Join operators
Aggregate functions.
Grouping operator
Relational Calculus concepts
Relational algebra queries for data retrieval with sample relational schemas. relational algebra operations.
EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION O...cscpconf
Classification is a step by step practice for allocating a given piece of input into any of the given
category. Classification is an essential Machine Learning technique. There are many
classification problem occurs in different application areas and need to be solved. Different
types are classification algorithms like memory-based, tree-based, rule-based, etc are widely
used. This work studies the performance of different memory based classifiers for classification
of Multivariate data set from UCI machine learning repository using the open source machine
learning tool. A comparison of different memory based classifiers used and a practical
guideline for selecting the most suited algorithm for a classification is presented. Apart fromthat some empirical criteria for describing and evaluating the best classifiers are discussed
In this paper firstly I have compared Single Label Text Categorization with Multi Label Text Categorization in detail then I have compared Document Pivoted Categorization with Category Pivoted Categorization in detail. For this purpose I have given the general definition of Text Categorization with its mathematical notation for the purpose of its frugality and cost effectiveness. Then with the help of mathematical notation and set theory ,I have converted the general definitions of Single Label Text Categorization and Multi Label Text Categorization into their respective mathematical representation .Then I discussed Binary Text Categorization as a special case of Single Label Text Categorization. After comparison of Single Label Text Categorization with Multi Label Text Categorization, I found that Single Label Text Categorization or Binary Text Categorization is more general than Multi Label Text Categorization. Thereafter I discussed an algorithm for transformation of Multi Label Classification into Binary Classification and explained the conditions of transformation of Multi Label Classification into Binary Classification. In the second step I compared Document Pivoted Categorization with Category Pivoted Categorization in detail. After comparison we found that Category Pivoted Categorization is more typical and complex than Document Pivoted Categorization. The Category Pivoted Categorization becomes more complicated when new category is added to predefined set of categories and the recurrent classification of documents takes place. Finally I compared Hard Categorization with Ranking Categorization. After comparing them I found that Hard Categorization incorporates ‘Hard Decisions’ about the relevance or belonging of a document to a category. This hard decision is either completely true or completely false. Whereas the Ranking Categorization creates a belonging of a document to a category
according to the estimated appropriateness to the document. The final Ranked List is developed in the Ranking Categorization which is used by the human expert for final decision of Text Categorization.
Feature selection on boolean symbolic objectsijcsity
With the boom in IT technology, the data sets used in
application are more and more larger and are
described by a huge number of attributes, therefore, the feature selection become an important discipline in
Knowle
dge discovery and data mining, allowing the experts
to select the most relevant features to impr
ove
the quality of their studies and to reduce the time processing of their algorithm. In addition to that, the data
used by the applications become richer. They are now represented by a set of complex and structured
objects, instead of simple numerical ma
trixes. The purpose of
our
algorithm is to do feature selection on
rich data,
called Boolean Symbolic Objects
(BSOs)
. These objects are desc
ribed by multivalued features.
The
BSOs
are considered as higher level units which can model complex data, such as c
luster of
individuals, aggregated data or taxonomies. In this paper we will introduce a new feature selection
criterion for
BSOs
, and we will explain
how we improved its complexity.
The Statement of Conjunctive and Disjunctive Queries in Object Oriented Datab...Editor IJCATR
Entrance of object orienting concept in database caused the relation database gradually to replace with object oriented
database in various fields. On the other hand for solving the problem of real world uncertain data, several methods were presented.
One of these methods for modeling database is an approach wich couples object-oriented database modeling with fuzzy logic. Many
queries that users to pose are expressed on the basis of linguistic variables. Because of classical databases are not able to support these
variables, leads to fuzzy approaches are considered. We investigate databases queries in this study both simple and complex ways. In
the complex way, we use conjunctive and disjunctive queries. In the following, we use the XML labels to express inqueries into fuzzy.
We can also communicate with other sections of software by entering into XML world as the most reliable opportunity. Also we want
to correct conjunctive and disjunctive queries related to fuzzy object oriented database using the concept of dependency measure and
weight, and weight be assigned to different phrases of a query based on user emphasis. The other aim of this research is mapping fuzzy
queries to fuzzy-XML. It is expected to be simple implement of query, and output of execution of queries be greatly closer to users'
needs and fulfill her expect. The results show that the proposed method explains the possible conjunctive and disjunctive queries the
database in the form of Fuzzy-XML.
Expression of Query in XML object-oriented databaseEditor IJCATR
Upon invent of object-oriented database, the concept of behavior in database was propounded. Before, relational database only provided a logical modeling of data and paid no attention to the operations applied on data in the system. In this paper, a method is presented for query of object-oriented database. This method has appropriate results when the user explains restrictions in a combinational matter (disjunctive and conjunctive) and assumes a weight for each one of restrictions based on their importance. Later, the obtained results are sorted based on their belonging rate to the response set. In continue, queries are explained using XML labels. The purpose is simplifying queries and objects resulted from queries to be very close to the user need and meet his expectation.
Expression of Query in XML object-oriented databaseEditor IJCATR
Upon invent of object-oriented database, the concept of behavior in database was propounded. Before, relational database
only provided a logical modeling of data and paid no attention to the operations applied on data in the system. In this paper, a method
is presented for query of object-oriented database. This method has appropriate results when the user explains restrictions in a
combinational matter (disjunctive and conjunctive) and assumes a weight for each one of restrictions based on their importance. Later,
the obtained results are sorted based on their belonging rate to the response set. In continue, queries are explained using XML labels.
The purpose is simplifying queries and objects resulted from queries to be very close to the user need and meet his expectation.
Expression of Query in XML object-oriented databaseEditor IJCATR
Upon invent of object-oriented database, the concept of behavior in database was propounded. Before, relational database
only provided a logical modeling of data and paid no attention to the operations applied on data in the system. In this paper, a method
is presented for query of object-oriented database. This method has appropriate results when the user explains restrictions in a
combinational matter (disjunctive and conjunctive) and assumes a weight for each one of restrictions based on their importance. Later,
the obtained results are sorted based on their belonging rate to the response set. In continue, queries are explained using XML labels.
The purpose is simplifying queries and objects resulted from queries to be very close to the user need and meet his expectation.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
1. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
1363
www.ijarcet.org
Abstract— The emerging application domains in Engineering,
Scientific Technology, Multimedia, GIS, Knowledge
management, Expert system design etc require advanced data
models to represent and manipulate the data values, because the
information resides in these domains are often vague or
imprecise in nature & difficult to represent while implementing
the application software. In order to fulfill the requirements of
such application demands, researchers have put the innovative
concept of object based fuzzy database system by extending the
object oriented system and adding fuzzy techniques to handle
complex object and imprecise data together. Some extensions of
the OODMS have been proposed in the literature, but what is
still lacking a unifying & systematic formalization of these
dedicated concepts. This paper is the consequence research of
our previous work, in which we proposed an effective & formal
Fuzzy class model to represent all type of fuzzy attributes &
objects those can be confined to fuzzy class. Here, we introduce a
generalized definition language for the fuzzy class which can
efficiently define the proposed fuzzy class model along with all
possible fuzzy data type to describe the structure of the database
& thus serve as data definition language for the object based
fuzzy database system.
Index Terms— Fuzzy class definition language, Fuzzy data
type, Fuzzy class, Object based fuzzy database model.
I. INTRODUCTION
The advancement in the requirements for modeling &
manipulation of complex object and imprecise information in
various knowledge intensive applications are emerging as
leading problems to the database research. The involvement
of complex object and vague information together make the
relational model & its extensions, to be apart from modeling
of such object or information. Object oriented data models are
widely acknowledged at the information modeling arena as
they provide hierarchical data abstraction scheme &
mechanisms for information hiding [6]. However, they are
incapable of representing or manipulating imprecise data
values. Mean while, probability theory & fuzzy logic provide
measures and rules for representing uncertain imprecise
information [2]; that has led to intensive research &
development of a high standard database system named
Manuscript received April, 2013.
Debasis Dwibedy, School of Computer Engineering,KIIT University
Bhubaneswar,Odisha. Bhubaneswar, India, +918763992183
Dr. Laxman Sahoo, Professor and Head of Database Group,KIIT
University , Bhubaneswar, India, +919692259550.
Sujoy Dutta, School of Computer Engineering, KIIT University,
Bhubaneswar, India, +919938077804.
“Object based fuzzy Database system”. The fuzzy object
modeling is being extensively studied to make it a knowledge
representation tool at various knowledge and large data
intensive applications with inherent fuzzy reasoning
techniques incorporating into it [14]. All the concepts
regarding fuzzy class, fuzzy attributes, fuzzy object class
relation and fuzzy inheritance stated in the literature are
specific and applicable for particular application domains
[8],[9],[12],[16]. The lacking of formalization of the existing
interpretations of fuzzy class, fuzzy object, fuzzy subclass-
super class relationships are exerting problems in determining
fuzziness at various levels of class hierarchy or establishing
fuzziness at inheritance and multiple inheritance structure.
So, to overcome such issues, we have thoroughly
investigated the current research proceedings & put an
attempt to redefine some concepts to make them more
prominent. In this regard, we first introduced the definition of
a generalized fuzzy class along with an efficient model to
represent the fuzzy class. Here, we extend our ongoing
research and propose a generalized fuzzy class definition
language to define the proposed fuzzy class model specifying
the data type and possible values of fuzzy attributes. The
various sections of the paper are organized as follows. In the
next section, we discuss about various research work carried
out to define the fuzzy class structure. In 3rd
section we
provide a glimpse of our previous contribution of designing a
generalized fuzzy class structure. In section 4, a formal
definition language for defining fuzzy class along with fuzzy
data type are provided & finally section 5 will take us to the
conclusion of this study.
II. RELATED WORK
There is little research in the development of fuzzy object
database system which addresses the practical perspective.
All the models or concepts stated in the literature are
theoretical or analytical in nature. We have investigated the
current research and development of fuzzy object based
database systems and outlined the concepts proposed by the
active researchers.
In [8], the author defined fuzzy class as fuzzy type whose
structural part is fuzzy structure. That means all the attributes
defined for a class should belongs to the class with certain
membership degree. A two layer graphical structure is also
proposed in the paper where the author used fuzzy class to
define instantiation and inheritance mechanism by the
principle of α-cut. An informal definition of fuzzy type is also
A Generalized Definition Language for
Implementing the Object Based Fuzzy Class
Model
Debasis Dwibedy, Dr. Laxman Sahoo, Sujoy Dutta
3. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
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www.ijarcet.org
III. OUR PREVIOUS CONTRIBUTION
We addressed the fuzzy class as a specialized crisp class
with an added linguistic label which comprises of general
attributes or crisp attributes, fuzzy attributes and iterative
attribute or special object [3].
A fuzzy class must contain either all of the given attributes or
some of the given attributes.
We introduced the concept of “Iterative fuzzy attribute or
Special object”. An iterative fuzzy attribute is an attribute or
special object which is having its own properties or attributes.
It is quite often seen in many applications, where we have
classes consist of attribute which can be decomposed into
further more simplified attributes. The existing fuzzy object
models do not provide any interface to represent or
manipulate such an attribute, which shows their lacking in
uniform formalization towards the global representations of
fuzzy class at any circumstances.
The representation of a new fuzzy class structure along with
fuzzy iterative attribute is given as follows:
We represented such a fuzzy class by two dashed line class
diagrams with little modifications of general object oriented
class diagram. For example, an application demands to
represent all the departments of our country into three distinct
categories: HIGHRANKEDDEPT,
MEDIUMRANKEDDEPT, and LOWRANKEDDEPT. All
these classes are specialized classes of the class DEPT and are
associated with a linguistic label which clearly indicates their
fuzziness.
Fig I shows the representation of a fuzzy class
HIGHRANKEDDEPT. The proposed model of fuzzy class
consists of two dashed rectangles each divided into two parts.
The first rectangle represents the fuzzy class whose name
placed at top of it, the first part of the rectangle shows the
membership degree of the fuzzy class belongs to the data
model or its membership degree to the super class if it is the
sub class and is represented by the symbol” λ” .The second
part of the rectangle represents all type of attributes possible
for the fuzzy class. A general attribute is represented as:
ATTRIBUTE NAME.
An attribute which takes value from a fuzzy domain like AGE
which might take fuzzy values as young, middle aged, old etc
is represented as:
FUZZY ATTRIBUTE NAME.
An attribute whose value is uncertain or imprecise is
represented as:
ATTRIBUTENAME WITH m DEGREE.
For example, all the departments may or may not have their
own library so we can write LIBRAY WITH 0.8 DEGREE.
A fuzzy iterative attribute is represented as:
ATTRIBUTE NAME *.
For example, EMPLOYEE *.
The second dashed rectangle represents a fuzzy iterative
attribute along with its associated properties. The first part of
the rectangle shows the membership degree of the fuzzy
iterative attribute to the fuzzy class and is represented as:
µattribute name*.
The second part of the rectangle represents the properties
of the fuzzy iterative attribute headed by the name of fuzzy
iterative attribute. The fuzzy class and its fuzzy iterative
attribute are associated with a dashed arrow labelled with
ITERATIVE *. If the fuzzy iterative attribute contains
another iterative attribute then it can also be represented
through another dashed rectangle of same type and the
association between these two attributes can be represented as
a dashed arrow labelled with ITERATIVE **.
The proposed model is flexible enough to represent and
manipulate a fuzzy class in a more efficient way considering a
wider range of possibilities of fuzziness in the classes to cater
services to diversified application domains. The model
strictly follows the ODMG guidelines and is easy to
implement. The portability inside the model will also
encourage adding more features as per requirements. Above
all, the model is very simple and easy to understand and it can
surely serve as a conceptual modelling for object based fuzzy
database. In the next section, we show the extension of the
research by putting the concepts of fuzzy data types and
designing an efficient framework for fuzzy class definition
language.
IV. EXTENSION OF THE RESEARCH
A. Data Types for Fuzzy Attributes
Data type is essential for uniform categorization of
attributes while defining the class of the attribute or object [5].
The data type of a fuzzy attribute depends up on the nature of
the attribute value and domain from which the attribute takes
its value [14]. Fuzzy attributes can be broadly classified into
two categories i.e fuzzy attributes whose fuzzy values are
fuzzy sets and fuzzy attributes whose values are fuzzy degrees
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International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
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fuzzy object database design and modeling. First, we have
redefined the fuzzy class definition or fuzzy class structure
and designed a uniform model to represent all type of fuzzy
attributes or objects at various levels of applicability. We
have extended the concept to practically implement the
proposed model by exploring the concept of basic fuzzy data
types to categorically address all type of fuzzy attributes and
also describing the nature their values. The proposed data
types can also serve as basic type for developing high
standard derived fuzzy data types. The fuzzy class definition
language is the result of the fuzzy class model defined earlier
and the proposed fuzzy data types; the portability inside the
language will allow the researchers to add more features at
changing application demands. The language is organized in
such a way that it can describe the structure of the fuzzy object
database in more prominent manner. We will extend the
research further to define and manipulate fuzzy inheritance
structure, fuzzy casual relations, fuzzy exception handling
and also emphasizes on designing an algebra for fuzzy object
query and processing of the query. The quest will be on its
way till the complete formalization of object based fuzzy
database system.
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Debasis Dwibedy received his B.Tech degree in
the year 2010 from BIET, Bhadrak affiliated to BPUT University, Odisha
and currently pursuing M.Tech in Computer Science Engineering from KIIT
University, Bhubaneswar. He was involved in Java application based project
Banking automation System during his B.Tech. He has produced many
papers in international journals..His research area includes Database
systems, Fuzzy Object Databases, Object Modeling and soft computing.
Dr. Laxman Sahoo received his Ph.D in 1987
from IIT, Kharagpur. Presently, he is professor and head of Database Engg.
Group at KIIT University, Odisha. He served as Director/Coordinator/Head
of Department in BITS Pilani, BITS Ranchi and Lucknow Indian Engg.
College. He has guided over 400 Master Degree students. He is associated
with many professional bodies as a member/chair person of technical
committee and conference. He has published and presented many Research
papers in national and international Journals. He is also author of a good
number of computer related books. His research area includes VLSI design,
DBMS, AI and Fuzzy Expert Systems.
Sujoy Dutta received his B.Tech degree in the year
2010 from WBUT University, West Bengal and currently pursuing M.Tech
in Computer Science Engineering from KIIT University, Bhubaneswar. He
has the research aptitude towards Fuzzy object database modeling and soft
computing.