Rough Neural Computing in Signal Analysis

This paper introduces an application of a particular form of rough neural computing in signal analysis. The form of rough neural network used in this study is based on rough sets, rough membership functions, and decision rules. Two forms of neurons are found in such a network: rough membership function neurons and decider neurons. Each rough membership function neuron constructs upper and lower approximation equivalence classes in response to input signals as an aid to classifying inputs. In this paper, the output of a rough membership function neuron results from the computation performed by a rough membership function in determining degree of overlap between an upper approximation set representing approximate knowledge about inputs and a set of measurements representing certain knowledge about a particular class of objects. Decider neurons implement granules derived from decision rules extracted from data sets using rough set theory. A decider neuron instantiates approximate reasoning in assessing rough membership function values gleaned from input data. An introduction to the basic concepts underlying rough membership neural networks is briefly given. An application of rough neural computing in classifying the power system faults is considered.     

Using narratives in conferences to improve the CMC learning environment

Abstract This paper reports on the use of short stories in Internet discussions to promote student learning. It describes off-campus teacher education students CMC discussions of short stories concerning issues in human development. The content of students' discussions is analysed, as is their perceptions of the value of the discussion stories. The results indicate that the use of narratives can improve the social environment of online conferences and contribute to collaborative student learning.     

Investigation of soybean oil as a diesel fuel extender: Endurance tests

Engine performance and crankcase lubricant viscosity were followed with 1∶2 and 1∶1 fuel mixtures of degummed soybean oil in No. 2 diesel fuel in tests with a John Deere 6-cylinder, 404 cubic in. displacement, direct-injection, turbocharged engine for a total of 600 running hours. A crankcase oil contamination problem resulting in an unacceptable thickening and a potential for gelling did exist with a 50/50 blend or a greater concentration of soybean oil, but it did not occur with the 1∶2 blend. The data accumulated during the initial 600 hr running time indicates that a fuel blend of one-third degummed soybean oil and two-thirds No. 2 diesel (1∶2 blend) may be a suitable fuel for agricultural equipment during periods of diesel fuel shortages or allocations. Additional data are being accumulated and will be analyzed in the future.     

Reinforcement learning to adjust parametrized motor primitives to new situations

Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR?6.     

Applying EuroWordNet to Cross-Language Text Retrieval

We discuss ways in which EuroWordNet (EWN) can be used in multilingual information retrieval activities, focusing on two approaches to Cross-Language Text Retrieval that use the EWN database as a large-scale multilingual semantic resource. The first approach indexes documents and queries in terms of the EuroWordNet Inter-Lingual-Index, thus turning term weighting and query/document matching into language-independent tasks. The second describes how the information in the EWN database could be integrated with a corpus-based technique, thus allowing retrieval of domain-specific terms that may not be present in our multilingual database. Our objective is to show the potential of EuroWordNet as a promising alternative to existing approaches to Cross-Language Text Retrieval.     

Goal-driven automation of a deep space communications station: a case study in knowledge engineering for plan generation and execution

Abstract: This paper describes the application of Artificial Intelligence techniques for plan generation, plan execution, and plan monitoring to automate a Deep Space Communication Station. This automation allows a communication station to respond to a set of tracking goals by appropriately reconfiguring the communications hardware and software to provide the requested communications services. In particular this paper describes: (1) the overall automation architecture, (2) the plan generation and execution monitoring AI technologies used and implemented software components, and (3) the knowledge engineering process and effort required for automation. This automation was demonstrated in February 1995, at the DSS13 Antenna Station in Goldstone, CA on a series of Voyager tracks and the technologies demonstrated are being transferred to the operational Deep Space Network stations.     

Review of ECSC research work in ironmaking

The Treaty of the European Coal and Steel Community (ECSC), which expired in 2002, states in its Article 55 to promote technical and economic research with respect to the production and growing application of coal and steel. The results of funded research projects shall be made available to all representatives and partners concerned. For the European steel industry Article 55 was the basis for building up a steel research programme covering topics from raw materials to steel application and instigated a European network of researchers from the steel industry, research institutes and universities. On the basis of these research and development activities carried out by the ECSC steel research, the iron and steel industry has shown important advances in technology and environmental compatibility. Furthermore, the ECSC steel research has contributed to nearly all important technological developments. The main objective of the ECSC steel Research and Technology Development (RTD) programme was to sustain and develop the Community steel industry's competitiveness. The following report describes fundamental developments and activities within the framework of ECSC research with particular respect to hot metal production in the last 15 years.