12858200
Building Robust Learning Systems by Combining Induction and Optimization
Each concept description language and search strategy has an inherent inductive bias, a preference for some hypotheses over others. No single inductive bias performs optimally on all problems. This paper describes a system that couples induction with optimization to carry out an efficient search of large regions of inductive bias space. Experimental results are reported demonstrating the system's capacity to choose optimal biases even for complex and noisy problems.
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>>
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.
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>>
28843712
Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts
This paper describes the Competitive Relation Learner (CRL), a generalized recursive splitting algorithm capable of producing a wide range of hybrid concept representations through the competitive application of multiple learning strategies, multiple decomposition strategies, and multiple decomposition evaluation strategies. Experimental results are reported that demonstrate CRL's ability to outperform several well known fixed-bias strategies.
208798481
A Discourse Model For Collaborative Design
A Discourse Model, including a structure and a process, is developed that provides software support for collaborative engineering design. Components of the structure include a workspace that incorporates frames, constraints, semantic networks, libraries of sharable design objects, agents, and a Virtual Workspace Language. Components of the process include procedures for identifying agent interest sets, applying state transformations to the design model, switching design contexts, identifying conflicts between designers, and tracking resolved conflicts. The model is implementation-independent and applicable to many research and commercial design environments currently available. An example scenario is provided in the Architecture/ Engineering/ Construction domain that illustrates collaboration during the conceptual design of a fire station.
42944978
Reliability enhancement of electronic packages by design of optimal parameters
Abstract Reliability growth testing involves the selection of optimal design parameters to enhance a product's reliability. This paper proposes a split plot experimental design that accommodates the restriction on randomization on the order of experimental runs caused by the experimental nature of accelerated reliability testing. The proposed experimental design provides statistically relevant solutions about the choice of design parameters, in terms of their reliability impact, in a much shorter time. A degradation model that aids in predicting the failure time for the given problem further supplements this discussion.
5694991
Robust Packet Scheduling in Wireless Cellular Networks
This paper addresses the following robust scheduling problem: Given that only coarse-grained channel state information (i.e., bounds on channel errors, but not the fine-grained error pattern) is available, how to design a robust scheduler that ensures worst-case optimal performance? To solve this problem, we consider two coarse-grained channel error models and take a zero-sum game theoretic approach, in which the scheduler and the channel error act as non-cooperative adversaries in the scheduling process. Our results show that in the heavy channel error case, the optimal scheduler adopts a threshold form. It does not schedule a flow if the price (the flow is willing to pay) is too small, in order to maximize the system revenue. Among the scheduled flows, the scheduler schedules a flow with a probability inversely proportional to the flow price such that the risk of being caught by the channel error adversary is minimized. We also show that in the mild channel error model, the robust scheduling policy exhibits a balanced trade-off between a greedy decision and a conservative policy. The scheduler is likely to take a greedy decision if it evaluates the risk of encountering the channel error adversary now to be small. Therefore, robust scheduling does not always imply conservative decision. The scheduler is willing to take “risks” to expect higher gain in some scenarios. Our solution also shows that probabilistic scheduling may lead to higher worst-case performance compared to traditional deterministic policies. Finally, the current efforts show the feasibility to explore a probabilistic approach to cope with dynamic channel error conditions.
3234528
An Ontology-Based Multimedia Annotator for the Semantic Web of Language Engineering
The development of the Semantic Web, the next-generation Web, greatly relies on the availability of ontologies and powerful annotation tools. However, there is a lack of ontology-based annotation tools for linguistic multimedia data. Existing tools either lack ontology support or provide limited support for multimedia. To fill the gap, we present an ontology-based linguistic multimedia annotation tool, OntoELAN, which features: (1) the support for OWL ontologies; (2) the management of language profiles, which allow the user to choose a subset of ontological terms for annotation; (3) the management of ontological tiers, which can be annotated with language profile terms and, therefore, corresponding ontological terms; and (4) storing OntoELAN annotation documents in XML format based on multimedia and domain ontologies. To our best knowledge, OntoELAN is the first audio/video annotation tool in the linguistic domain that provides support for ontology-based annotation. It is expected that the availability of such a tool will greatly facilitate the creation of linguistic multimedia repositories as islands of the Semantic Web of language engineering.
41350051
Language engineering for the Semantic Web: a digital library for endangered languages
23906830
PEAS: a robust energy conserving protocol for long-lived sensor networks
Small, inexpensive sensors with limited memory, computing power and short battery lifetimes are turning into reality. Due to adverse conditions such as high noise levels, extreme humidity or temperatures, or even destructions from unfriendly entities, sensor node failures may become norms rather than exceptions in real environments. To be practical, sensor networks must last for much longer times than that of individual nodes, and have yet to be robust against potentially frequent node failures. This paper presents the design of PEAS, a simple protocol that can build a long-lived sensor network and maintain robust operations using large quantities of economical, short-lived sensor nodes. PEAS extends system functioning time by keeping only a necessary set of sensors working and putting the rest into sleep mode. Sleeping ones wake up now and then, probing the local environment and replacing failed ones. The sleeping periods are self-adjusted dynamically, so as to keep the sensors' wakeup rate roughly constant, thus adapting to high node densities.