An adaptive nearest neighbor search for a parts acquisition ePortal

Abstract

One of the major hurdles in maintaining long-lived electronic systems is that electronic parts become obsolete, no longer available from the original suppliers. When this occurs, an engineer is tasked with resolving the problem by finding a replacement that is "as similar as possible" to the original part. The current approach involves a laborious manual search through several electronic portals and data books. The search is difficult because potential replacements may differ from the original and from each other by one or more parameters. Worse still, the cumbersome nature of this process may cause the engineers to miss appropriate solutions amid the many thousands of parts listed in industry catalogs.In this paper, we address this problem by introducing the notion of a parametric "distance" between electronic components. We use this distance to search a large parts data set and recommend likely replacements. Recommendations are based on an adaptive nearest-neighbor search through the parametric data set. For each user, we learn how to scale the axes of the feature space in which the nearest neighbors are sought. This allows the system to learn each user's judgment of the phrase "as similar as possible."

Incremental User Modeling With Heterogeneous User Behaviors

“…The evaluation of user modeling algorithm is based on our Model-Guided Inquiry (MGI) framework, which uses a user model to shape the behavior of an information system (Alonso et al 2003;Alonso & Li, 2005a, 2005b. Such systems are termed modelguided systems.…”

“…The benefits of model-guided inquiry have been demonstrated in our prior work. In one study, it was used to personalize a search for obsolete replacement parts (Alonso et al 2003). In another, encouraging results were obtained by using models to guide a text search mechanism based on swarm intelligence (Alonso & Li, 2005b).…”

Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

“…Each feature has a weight associated with it that indicates the opponent's preference for a feature in the context of IED emplacement activities. The feature weights are dynamically adapted with the latest IED events using function value estimation algorithms [5,6]. The cells are grouped into IED influence regions based on a cell's location and PIT value.…”

Biobjective Scheduling for Joint Parallel Machines with Sequence-Dependent Setup by Taking Pareto-Based Approach

“…In this study, it was further assumed that once started, a task could not be overridden or cancelled, until it was fully completed. To this end, the state-of-the-art computational intelligence algorithms, that is, adaptive nearest neighbour search (ANNS) [12,13] and modified tabu search (MTS) [14][15][16][17], were employed and assessed in turns. The resultant scheduling for small and large problems was subsequently validated against an exhaustive baseline model.…”