An Intelligent Knowledge Management System from a Semantic Perspective

Abstract. Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

Automated knowledge generation

The general objectives of the NASA/UCF Automated Knowledge Generation Project were the development of an intelligent software system that could access CAD design data bases, interpret them, and generate a diagnostic knowledge base in the form of a system model. The initial area of concentration is in the diagnosis of the process control system using the Knowledge-based Autonomous Test Engineer (KATE) diagnostic system. A secondary objective was the study of general problems of automated knowledge generation. A prototype was developed, based on object-oriented language (Flavors)

An expert system based intelligent control scheme for space bioreactors

An expert system based intelligent control scheme is being developed for the effective control and full automation of bioreactor systems in space. The scheme developed will have the capability to capture information from various resources including heuristic information from process researchers and operators. The knowledge base of the expert system should contain enough expertise to perform on-line system identification and thus be able to adapt the controllers accordingly with minimal human supervision

Comparison of Intelligent Fuzzy Controller and Fuzzy Rule Suram Algorithms in the Drying Process

Both the Intelligent Fuzzy Controller and Suram Fuzzy Rule are using a look-up table such as defuzzification analysis, which is based on weather variables, namely ambient temperature and ambient humidity for a drying process. State variable membership functions are expressed in terms of error values and error changes with typical triangular maps and trapezoidal maps. An Intelligent fuzzy controller is a hybrid controller which consists of an optimal fuzzy controller, fuzzy controller, and adaptive fuzzy controller. Membership function design is used to build the algorithm process. The algorithm process was developed based on the input-output knowledge pair for the drying process. The membership function must be stable and flexible with respect to weather and performance derived from the acquisition process, time constants, and system delays. The developed control system involves temperature control in different zones and ambient humidity. The system model was developed using a system identification scheme based on online input/output data and knowledge gathered through extensive testing. The knowledge base of fuzzy tuners is derived from drying schedules for certain wood specimens. The intelligent fuzzy control algorithm is used for scheduling the controller on various drying schedules. The results show that the proposed approach to overall control has great potential for performance improvement when applied to other industrial kilns. An intelligent fuzzy controller is also implemented, and its performance is compared with conventional controllers, it is more smooth, robust and controllable. On the other hand, the Suram fuzzy rule is an algorithm developed to control a drying system using diesel as an energy source by modifying the value of the fuzzy membership function [0.5,1]; and has been developed taking into account the wind speed in the drying process. The comparison results show that the Fuzzy Rule Suram is more efficient than the Intelligent Fuzzy Controller in terms of the use of electrical energy, by maximizing the use of solar energy

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by whichorganizations can better use information and, more importantly, manageknowledge. Unlike other strategies, knowledge management (KM) is difficult todefine because it encompasses a range of concepts, management tasks,technologies, and organizational practices, all of which come under the umbrella ofthe information management. Semantic approaches allow easier and more efficienttraining, maintenance, and support knowledge. Current ICT markets are dominatedby relational databases and document-centric information technologies, proceduralalgorithmic programming paradigms, and stack architecture. A key driver of globaleconomic expansion in the coming decade is the build-out of broadbandtelecommunications and the deployment of intelligent services bundling. This paperintroduces the main characteristics of an Intelligent Knowledge ManagementSystem as a multiagent system used in a Learning Control Problem (IKMSLCP),from a semantic perspective. We describe an intelligent KM framework, allowingthe observer (a human agent) to learn from experience. This framework makes thesystem dynamic (flexible and adaptable) so it evolves, guaranteeing high levels ofstability when performing his domain problem P. To capture by the agent who learnthe control knowledge for solving a task-allocation problem, the control expertsystem uses at any time, an internal fuzzy knowledge model of the (business)process based on the last knowledge model

A reusable knowledge acquisition shell: KASH

KASH (Knowledge Acquisition SHell) is proposed to assist a knowledge engineer by providing a set of utilities for constructing knowledge acquisition sessions based on interviewing techniques. The information elicited from domain experts during the sessions is guided by a question dependency graph (QDG). The QDG defined by the knowledge engineer, consists of a series of control questions about the domain that are used to organize the knowledge of an expert. The content information supplies by the expert, in response to the questions, is represented in the form of a concept map. These maps can be constructed in a top-down or bottom-up manner by the QDG and used by KASH to generate the rules for a large class of expert system domains. Additionally, the concept maps can support the representation of temporal knowledge. The high degree of reusability encountered in the QDG and concept maps can vastly reduce the development times and costs associated with producing intelligent decision aids, training programs, and process control functions

3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network

State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53 which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process