Autonomous power expert system

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control technologies to the Space Station Freedom Electrical Power Systems (SSF/EPS). The objectives of the program are to establish artificial intelligence/expert system technology paths, to create knowledge based tools with advanced human-operator interfaces, and to integrate and interface knowledge-based and conventional control schemes. This program is being developed at the NASA-Lewis. The APS Brassboard represents a subset of a 20 KHz Space Station Power Management And Distribution (PMAD) testbed. A distributed control scheme is used to manage multiple levels of computers and switchgear. The brassboard is comprised of a set of intelligent switchgear used to effectively switch power from the sources to the loads. The Autonomous Power Expert System (APEX) portion of the APS program integrates a knowledge based fault diagnostic system, a power resource scheduler, and an interface to the APS Brassboard. The system includes knowledge bases for system diagnostics, fault detection and isolation, and recommended actions. The scheduler autonomously assigns start times to the attached loads based on temporal and power constraints. The scheduler is able to work in a near real time environment for both scheduling and dynamic replanning

Autonomous power expert system

The Autonomous Power Expert (APEX) system was designed to monitor and diagnose fault conditions that occur within the Space Station Freedom Electrical Power System (SSF/EPS) Testbed. APEX is designed to interface with SSF/EPS testbed power management controllers to provide enhanced autonomous operation and control capability. The APEX architecture consists of three components: (1) a rule-based expert system, (2) a testbed data acquisition interface, and (3) a power scheduler interface. Fault detection, fault isolation, justification of probable causes, recommended actions, and incipient fault analysis are the main functions of the expert system component. The data acquisition component requests and receives pertinent parametric values from the EPS testbed and asserts the values into a knowledge base. Power load profile information is obtained from a remote scheduler through the power scheduler interface component. The current APEX design and development work is discussed. Operation and use of APEX by way of the user interface screens is also covered

A knowledge-based fair-share scheduler

Call number: LD2668 .R4 CMSC 1989 S23Master of ScienceComputing and Information Science

AGENT MEETING SCHEDULER

This dissertation is purposed to record all the data gathered throughout author's study and research for this project. A deep study of agent algorithm is conducted based on current available agent meeting scheduler from combination of software agent and algorithm data structure knowledge. The current problem of typical meeting scheduler is it is time consuming and inefficient; and also a resource needs to be allocated to perform the meeting scheduling job. Agent meeting scheduler will be used to replace this typical meeting scheduler to make it more efficient in term of deciding meeting time. The study is meant to research and select suitable algorithm to be implemented in agent meeting scheduler. An agent meeting scheduler prototype then will be developed to prove that the selected algorithm is working properly. Qualitative research method is being used to gather necessary data on agent algorithm and this data will be used to select the suitable algorithm. Through the research conducted on available algorithm for agent meeting scheduler, genetic algorithm is selected to be used in this project. The agent meeting scheduler prototype then will be developed by using PHP language. PHP is selected for its interactivity and extensibility

Approach to Job-Shop Scheduling Problem Using Rule Extraction Neural Network Model

This thesis focuses on the development of a rule-based scheduler, based on production rules derived from an artificial neural network performing job shop scheduling. This study constructs a hybrid intelligent model utilizing genetic algorithms for optimization and neural networks as learning tools. Genetic algorithms are used for obtaining optimal schedules and the neural network is trained on these schedules. Knowledge is extracted from the trained network. The performance of this extracted rule set is analyzed in scheduling a test set of 3x3 scheduling instances. The capability of the rule-based scheduler in providing near optimal solutions is also discussed in this thesis

A knowledge-based expert system for scheduling of airborne astronomical observations

The Kuiper Airborne Observatory Scheduler (KAOS) is a knowledge-based expert system developed at NASA Ames Research Center to assist in route planning of a C-141 flying astronomical observatory. This program determines a sequence of flight legs that enables sequential observations of a set of heavenly bodies derived from a list of desirable objects. The possible flight legs are constrained by problems of observability, avoiding flyovers of warning and restricted military zones, and running out of fuel. A significant contribution of the KAOS program is that it couples computational capability with a reasoning system

Teraphim: a domain-independent framework for constructing blackboard-controlled, blackboard-based expert systems in Prolog

The blackboard architecture, in which a set of independent knowledge sources communicate by means of a global data base known as a blackboard, has been suggested as a generally useful design for knowledge-based systems. Teraphim is a domain-independent frame work for writing blackboard-based expert systems in Prolog. It implements concepts common to a range of previous blackboard architecture programs, such as HEARSAY-III and BB1. Teraphim includes as its basic elements a partitioned blackboard, a simple blackboard-controlled scheduler, a set of general-purpose scheduling heuristics to control the scheduler, a generic knowledge source with the ability to ask the user questions about incomplete data, modifiable methods of reasoning about uncertain data, and a simple explanation facility that traces the origins of terms on the problem blackboard. Trials of the system indicate that it can be used to implement expert systems to solve either synthesis or analysis problems. The blackboard architecture of Teraphim lends itself to experimentation with the kinds of knowledge representation and control knowledge needed to solve problems. Prolog proved to be a convenient language for writing blackboard-based systems

Dynamic Face Video Segmentation via Reinforcement Learning

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at: https://github.com/mapleandfire/300VW-Mas