1764244
Machine Learning in Engineering Automation
Abstract Engineers need intelligent tools to assist them with problems such as design, planning, monitoring, control, diagnosis, and analysis. Manual construction of these tools can be costly or impossible due to problems such as large amounts of data, lack of problem understanding, and the expense of knowledge engineering. Machine learning techniques hold promise for assisting in solutions to many of these problems, but engineering domains present significant challenges to learning systems, including: noisy data, continuous quantities, mathematical formulas, large problem spaces, incorporating multiple sources and forms of knowledge, and the need for user-system interaction. This paper describes a number of challenges to learning systems motivated by engineering applications and describes a taxonomy of engineering tasks for application of machine learning technology.
37627056
SWIFT: system workbench for integrating and facilitating teams
There have been many R&D efforts in recent years to develop computer environments that directly enhance group problem-solving productivity. An example of such efforts is the computer-based virtual teaming technology (VTT) developed at the knowledge-based Engineering Sytems Research Laboratory (KESRL). The core software environment for KESRL's VTT is called SWIFT and is based on the knowledge processing technology (KPT) previously developed at KESRL to support various engineering design tasks. SWIFT is a flexible environment which enables the rapid development of collaborative applications, linking workers in distributed environments, of many domains. The paper presents the overall system architecture of SWIFT, and introduces the current and future R&D efforts at KESRL in realizing SWIFT for significant industrial applications.<<ETX>>
12297349
Creating and using models for engineering design: a machine-learning approach
An adaptive and interactive modeling system (AIMS) that integrates simulation, optimization and machine learning to help engineers make design decisions is described. AIMS views engineering decision making as a two-phase process of creating and then using models. The competitive relation learner and the induce-and-select optimizer, AIMS's two main components, and their roles in both phases of decision-making are discussed. AIMS's role in supporting the design of a diesel engine that outputs power within the 440- to 460-kW range and consumes the least amount of fuel is also discussed.<<ETX>>
6309018
Knowledge-based learning integrating acquisition and learning
Empirical learning algorithms are hampered by their inability to use domain knowledge to guide the induction of new rules. This paper describes knowledge-based learning, an approach to learning that selects the examples and relevant attributes for an empirical algorithm. Knowledge-based learning can be used for developing rules for engineering expert systems. Engineers often have some rules for problem solving, but also many experiences (examples) that facilitate solving problems. Knowledge-based learning systems are able to use both forms of knowledge.
35812356
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems
Abstract A knowledge-based learning system is developed to demonstrate that intelligently selecting a subset of examples based on domain knowledge for rule induction can be more productive than using all provided examples. Knowledge-based learning uses an incomplete domain theory and examples to induce knowledge missing in the theory. Knowledge-based learning systems determine where knowledge is missing in a domain theory, select a relevant subset of examples to use in induction, and induce new rules or modify existing rules.
5343942
Knowledge-Based Equation Discovery in Engineering Domains
Machine learning techniques that can compress datasets into relationships that underlie the data have great utility for engineers. Most model discovery systems are not well suited to discovering comprehensible models in engineering domains. In this paper we present KEDS, a Knowledge-based Equation Discovery System, which uses a model-driven approach to discover equations and thereby build models from engineering data.
620324
Relational Nested Optional Join for Efficient Semantic Web Query Processing
Increasing amount of RDF data on the Web drives the need for its efficient and effective management. In this light, numerous researchers have proposed to use RDBMSs to store and query RDF annotations using the SQL and SPARQL query languages. The first few attempts at SPARQL-to-SQL translation revealed non-trivial challenges related to correctness and efficiency of such translation in the presence of nested optional graph patterns. In this paper, we propose to extend relational databases with a novel relational operator, nested optional join (NOJ), that is more efficient than left outer join in processing nested optional graph patterns. We design three efficient algorithms to implement the new operator in relational databases: (1) nested-loops NOJ algorithm, NL-NOJ, (2) sort-merge NOJ algorithm, SM-NOJ, and (3) simple hash NOJ algorithm, SH-NOJ. Based on a real life RDF dataset, we demonstrate the efficiency of our algorithms by comparing them with the corresponding left outer join implementations.
172967
XML subtree reconstruction from relational storage of XML documents
Numerous researchers have proposed to use relational databases to store and query XML documents. In these systems, the elements selected by an XML query are returned to an application either by select mode or by reconstruct mode. For the reconstruct mode, the XML subtrees that are rooted at the selected elements need to be extracted and reconstructed from the relational storage of XML documents. Therefore, XML subtree reconstruction is an important problem since its efficiency has a significant impact on XML query response time. In this paper, we propose (i) a linear XML subtree reconstruction algorithm Reconstruct to reconstruct an XML subtree from the structure-encoded sequence of the subtree that is extracted from the relational database by a structure-encoded sequence retrieval algorithm, (ii) a generic efficient structure-encoded sequence retrieval algorithm RD-SB for a schema-based relational XML storage, and (iii) a generic efficient structure-encoded sequence retrieval algorithm RD-SL for a schema-less relational XML storage. To the best of our knowledge, our algorithms provide the first generic solutions to the XML subtree reconstruction problem that are applicable to all relational XML storage schemes proposed in the literature. Finally, our experiments show that our algorithms are efficient and scalable.
8338982
Self-coordinating localized fair queueing in wireless ad hoc networks
Distributed fair queueing in a multihop, wireless ad hoc network is challenging for several reasons. First, the wireless channel is shared among multiple contending nodes in a spatial locality. Location-dependent channel contention complicates the fairness notion. Second, the sender of a flow does not have explicit information regarding the contending flows originated from other nodes. Fair queueing over ad hoc networks is a distributed scheduling problem by nature. Finally, the wireless channel capacity is a scarce resource. Spatial channel reuse, i.e., simultaneous transmissions of flows that do not interfere with each other, should be encouraged whenever possible. In this paper, we reexamine the fairness notion in an ad hoc network using a graph-theoretic formulation and extract the fairness requirements that an ad hoc fair queueing algorithm should possess. To meet these requirements, we propose maximize-local-minimum fair queueing (MLM-FQ), a novel distributed packet scheduling algorithm where local schedulers self-coordinate their scheduling decisions and collectively achieve fair bandwidth sharing. We then propose enhanced MLM-FQ (EMLM-FQ) to further improve the spatial channel reuse and limit the impact of inaccurate scheduling information resulted from collisions. EMLM-FQ achieves statistical short-term throughput and delay bounds over the shared wireless channel. Analysis and extensive simulations confirm the effectiveness and efficiency of our self-coordinating localized design in providing global fair channel access in wireless ad hoc networks.
896953
Sequential Aggregate Signatures and Multisignatures Without Random Oracles
We present the first aggregate signature, the first multisignature, and the first verifiably encrypted signature provably secure without random oracles. Our constructions derive from a novel application of a recent signature scheme due to Waters. Signatures in our aggregate signature scheme are sequentially constructed, but knowledge of the order in which messages were signed is not necessary for verification. The aggregate signatures obtained are shorter than Lysyanskaya et al. sequential aggregates and can be verified more efficiently than Boneh et al. aggregates. We also consider applications to secure routing and proxy signatures.
2842384
TCP Friendly Rate Adaptation for Multimedia Streaming in Mobile ad hoc Networks
Transport protocol for supporting multimedia streaming in mobile ad hoc networks has to cope with the rich dynamics, such as mobility-induced disconnection and reconnection, high out-of-order delivery ratios, channel errors, and network congestion. In this work, we design and implementate ADTFRC, a TCP-friendly transport protocol for ad hoc networks. ADTFRC adapts wireline TFRC protocol to ad hoc networks with improved rate adaptation behavior, the capability of application layer framing and selective retransmission. ADTFRC detects different packet loss behaviors based on end-to-end measurements of multiple metrics. This allows ADTFRC to more accurately gauge the network behavior and achieve higher throughput. Simulations show that the performance of ADTFRC is higher than standard TFRC and TCP with explicit-link-failure-notification (ELFN) support in terms of throughput, rate adaptation behavior and application level quality while still maintaining the TCP-Friendliness property.