Toward a domain-specific heuristic knowledge based spectrum reconstruction method for multispectral camera with CMOS Fabry-Perot interferometer

2018 ◽  
Author(s):  
Anna Zhao ◽  
Chen Zhang ◽  
Shuyang Liu ◽  
Tao Zhao ◽  
Xiaodong Jia
Keyword(s):  
Reconstruction Method ◽  
Heuristic Knowledge ◽  
Spectrum Reconstruction ◽  
Domain Specific ◽  
Knowledge Based ◽  
Fabry Perot ◽  
Multispectral Camera

scholarly journals A META-TOOL TO SUPPORT THE DEVELOPMENT OF KNOWLEDGE ENGINEERING METHODOLOGIES AND PROJECTS

2012 ◽  
Vol 22 (08) ◽  
pp. 1055-1083
Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ
Keyword(s):  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Knowledge Engineering ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Combined Application ◽  
Processing Information ◽  
Knowledge Acquisition Methods ◽  
Applied Knowledge

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

scholarly journals ONTOLOGY-BASED WEB TOOLS FOR RETRIEVING PHOTOGRAMMETRIC CULTURAL HERITAGE MODELS

2019 ◽  
Vol XLII-2/W10 ◽  
pp. 31-38 ◽  
Author(s):  
M. Ben Ellefi ◽  
P. Drap ◽  
O. Papini ◽  
D. Merad ◽  
J. P. Royer ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Web Tools ◽  
Domain Specific Knowledge ◽  
Visualization Systems ◽  
User Friendly ◽  
Machine Readable ◽  
Associated Knowledge

<p><strong>Abstract.</strong> A key challenge in cultural heritage (CH) sites visualization is to provide models and tools that effectively integrate the content of a CH data with domain-specific knowledge so that the users can query, interpret and consume the visualized information. Moreover, it is important that the intelligent visualization systems are interoperable in the semantic web environment and thus, capable of establishing a methodology to acquire, integrate, analyze, generate and share numeric contents and associated knowledge in human and machine-readable Web. In this paper, we present a model, a methodology and a software Web-tools that support the coupling of the 2D/3D Web representation with the knowledge graph database of <i>Xlendi</i> shipwreck. The Web visualization tools and the knowledge-based techniques are married into a photogrammetry driven ontological model while at the same time, user-friendly web tools for querying and semantic consumption of the shipwreck information are introduced.</p>

Stability of Number-Processing Based on Information Granularity Associative with Parietal Cortex

2014 ◽  
Vol 519-520 ◽  
pp. 769-774
Author(s):  
Xiu Zhen Wang ◽  
Ri Feng Wang ◽  
Jian Hui Chen ◽  
Wei Quan Gu ◽  
Yue Gu ◽  
...  
Keyword(s):  
Parietal Lobe ◽  
Number Line ◽  
Information Granularity ◽  
Granule Formation ◽  
Domain Specific ◽  
Knowledge Based ◽  
Unit Structure ◽  
Automatic Information ◽  
Arithmetic System ◽  
Cognitive Arithmetic

Stability, the ability to automatically extract and produce the efficient and accurate results of a defined problem without making epistemic assumptions, is discussed here as a possible memory system for understanding complex cognitive functions of the arithmetical learning. Stability is of top priority because it may typify organization of granule (knowledge-based information unit) structure. Memory efficiencies are that they depend on both linguistic factors and exposure to arithmetic training during granule formation or consolidation, supporting the idea of analog coding of numerical representations. Neuroimaging studies suggest that the parietal lobe as a potential substrate for a domain-specific representation of numeric quantities and associative memory mechanisms in stability, and results from these studies indicate that there may be the organization of number-related processes of stability in the parietal lobe. Stability seems to depend on the automatic information-processing system's response to experiential knowledge combining granularity (degree of detail or precision), maturational constraints, spatial factors (mental number line) and linguistic factors, making it an ideal candidate for understanding how these interactions play out in the cognitive arithmetic system.

Using Features to Support Finite Element Idealizations

1994 ◽  
Author(s):  
Padmanabh Dabke ◽  
Vallury Prabhakar ◽  
Sheri Sheppard
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Spatial Reasoning ◽  
Element Model ◽  
Heuristic Knowledge ◽  
Knowledge Based System ◽  
Spatial Properties ◽  
Knowledge Based ◽  
Feature Based ◽  
The Finite Element Model

Abstract This paper describes how feature-based techniques can be used in a knowledge-based system to support finite element idealizations. Any system of this kind must have two important features. First, it must capture the experiential and heuristic knowledge used by expert analysts in making idealization decisions. Second, the system must be able to perform spatial reasoning about the finite element model being analyzed. The first requirement led us to incorporate knowledge-based reasoning in the idealization systems. We chose the formalism of “features” to capture the spatial reasoning because expert analysts often describe the idealization process in terms of removing / modifying features (such as holes, slots, notches, etc.) and their spatial properties.

Word Sense Disambiguation

2013 ◽  
pp. 22-51
Author(s):  
Pushpak Bhattacharyya ◽  
Mitesh Khapra
Keyword(s):  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Current Trend ◽  
General Purpose ◽  
Word Sense ◽  
Domain Specific ◽  
Knowledge Based ◽  
Current State ◽  
Sense Disambiguation ◽  
State Of Affairs

This chapter discusses the basic concepts of Word Sense Disambiguation (WSD) and the approaches to solving this problem. Both general purpose WSD and domain specific WSD are presented. The first part of the discussion focuses on existing approaches for WSD, including knowledge-based, supervised, semi-supervised, unsupervised, hybrid, and bilingual approaches. The accuracy value for general purpose WSD as the current state of affairs seems to be pegged at around 65%. This has motivated investigations into domain specific WSD, which is the current trend in the field. In the latter part of the chapter, we present a greedy neural network inspired algorithm for domain specific WSD and compare its performance with other state-of-the-art algorithms for WSD. Our experiments suggest that for domain-specific WSD, simply selecting the most frequent sense of a word does as well as any state-of-the-art algorithm.

Human Cognitive Processes

2009 ◽  
pp. 1-33
Author(s):  
Slava Kalyuga
Keyword(s):  
Working Memory ◽  
Cognitive Architecture ◽  
Human Cognition ◽  
Long Term Memory ◽  
Domain Specific ◽  
Knowledge Based ◽  
Domain Specific Knowledge ◽  
And Performance ◽  
Memory Knowledge ◽  
High Level

One of the major components of our cognitive architecture, working memory, becomes overloaded if more than a few chunks of information are processed simultaneously. For example, we all experience this cognitive overload when trying to keep in memory an unfamiliar telephone number or add two four-digit numbers in the absence of a pen and paper. Similar in nature processing limitations of working memory represent a major factor influencing the effectiveness of human learning and performance, particularly in complex environments that require concurrent performance of multiple tasks. The learner prior domain-specific knowledge structures and associated levels of expertise are considered as means of reducing these limitations and guiding high-level knowledge-based cognitive activities. One of the most important results of studies in human cognition is that the available knowledge is a single most significant learner cognitive characteristic that influences learning and cognitive performance. Understanding the key role of long-term memory knowledge base in our cognition is important to the successful management of cognitive load in multimedia learning.

Multi-Agent Architecture for Knowledge-Driven Decision Support

2010 ◽  
pp. 28-46
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Intelligent Agents ◽  
Agent Architecture ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Intelligent Decision ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers. Increasingly, artificial intelligence (AI) techniques for knowledge representation are being used to deliver knowledge-driven decision support in multiple forms. In this chapter, we present an Architecture for knowledge-based decision support, delivered through a Multi-Agent Architecture. We illustrate how to represent and exchange domain-specific knowledge in XML-format through intelligent agents to create exchange and use knowledge to provide intelligent decision support. We show the integration of knowledge discovery techniques to create knowledge from organizational data; and knowledge repositories (KR) to store, manage and use data by intelligent software agents for effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

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