14293479
Equation generation for clustering
Clustering events into useful classifications is an important part of problem solving in engineering. This paper discusses the role of clustering in engineering and why numerical and conceptual clustering techniques are not always adequate. The knowledge based equation generator system was constructed to overcome one of these shortfalls. This system partitions events based upon mathematical formulas used to describe the events.
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.
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.
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>>
7682350
Incremental genetic K-means algorithm and its application in gene expression data analysis
BackgroundIn recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.ResultsIn this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm.ConclusionsOur experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
11578218
A new model for packet scheduling in multihop wireless networks
The goal of packet scheduling disciplines is to achieve fair and maximum allocation of channel bandwidth. However, these two criteria can potentially be in conflict in a generic-topology multihop wireless network where a single logical channel is shared among multiple contending flows and spatial reuse of the channel bandwidth is possible. In this paper, we propose a new model for packet scheduling that addresses this conflict. The main results of this paper are the following: (a) a two-tier service model that provides a minimum “fair” allocation of the channel bandwidth for each packet flow and additionally maximizes spatial reuse of bandwidth, (b) an ideal centralized packet scheduling algorithm that realizes the above service model, and (c) a practical distributed backoff-based channel contention mechanism that approximates the ideal service within the framework of the CSMA/CA protocol.
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.
16187298
DIRAC: a software-based wireless router system
Routers are expected to play an important role in the IP-based wireless data network. Although a substantial number of techniques have been proposed to improve wireless network performance under dynamic wireless channel conditions and host mobility, a system support framework is still missing. In this paper, we describe DIRAC, a software-based router system that is designed for wireless networks to facilitate the implementation and evaluation of various channel-adaptive and mobility-aware protocols. DIRAC adopts a distributed architecture that is composed of two parts: a Router Core (RC) shared by the wireless subnets, and a Router Agent (RA) at each access point/base station. RAs expose wireless link-layer information to the RC and enforce the control commands issued by the RC. This approach allows the router to make adaptive decisions based on link-layer information feedback. It also permits the router to enforce its policies (e.g., policing) more effectively through underlying link-layer mechanisms. As showcases, we implement under DIRAC the prototypes of three wireless network services: link-layer assisted fast handover, channel-adaptive scheduling, and link-layer enforced policing. Our implementation and experiments show that our distributed wireless router provides a flexible framework, which enables advanced network-layer wireless services that are adaptive to channel conditions and host mobility.
17978140
Completeness and realizability: conditions for automatic generation of workflows
In recent years, workflow technology has greatly facilitated business process modeling and reengineering in information systems. On one hand, the separation of an application's control structure from the implementation of its task programs simplifies and speeds up application development. On the other hand, the run-time system assists users in coordinating and scheduling tasks of a business process. As a result, the cost of doing business is reduced, and business enterprises become more responsive to new business requirements and opportunities, hence, become more competitive in the market. Several formal methods have been proposed for specifying and modeling workflows. However, most workflows are designed manually, which becomes a time-consuming and error-prone procedure when a workflow involves hundreds or thousands of task programs. A recent approach is to generate workflow automatically from a task library and a specification of the desired outcome. In this paper, we define the notions of positive and negative dependency graphs for a task library to represent the dependencies among tasks in the library. Based on these dependencies, we identified sufficient conditions for a task library to be complete, and the conditions for a workflow postcondition to be realizable from a task library. We also identified a sufficient and necessary condition for the completeness of a class of task libraries, called S-task libraries.