12408507
A Rough Set Approach to Attribute Generalization in Data Mining
Abstract This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification rules from very large data bases generalized by dynamic conceptual hierarchies provided by users. In general, the process of attribute generalization may introduce inconsistency into a generalized relation. This issue is resolved by using the inductive learning algorithm, LERS based on rough set theory.
18544921
Active User-Based and Ontology-Based Web Log Data Preprocessing for Web Usage Mining
User identification and session identification are two major steps in preprocessing Web log data for Web usage mining. This paper introduces a fast active user-based user identification algorithm with time complexity O(n). The algorithm uses both an IP address and a finite users' inactive time to identify different users in the Web log. Web site ontology is useful for identifying Web site structure and break points for browsing behavior. For session identification, we present an ontology-based method that utilizes the Web site structure and functionalities to identify different sessions
30635651
A criterion for a multi-dimensional explosion due to a concentrated nonlinear source
Abstract This article studies a multi-dimensional parabolic first initial–boundary value problem with a concentrated nonlinear source. A criterion for its solution to blow up everywhere on the concentrated source in a finite time is given.
5780401
Inverse modeling using multi-block PLS to determine the environmental conditions that provide optimal cellular function
MOTIVATIONS
Tissue engineering constitutes an important field with its potential of addressing the current shortage in organ availability. To successfully develop tissue-engineered organs, it is crucial to understand how to maintain the cells under conditions that maximize their ability to perform their physiological roles, regardless of the environment, whether the cells are part of an extracorporeal system, such as the bioartificial liver assist device, or an implantable tissue-engineered device. Our goals are to (1) provide insight into how cells will behave when confronted with changes in its environment and (2) determine the optimal environmental factors to achieve a desired level of cellular function.


RESULTS
Diverse sets of environmental factors were used to systematically perturb the metabolic behavior associated with pre-conditioning and plasma supplementation. To probe metabolic state of hepatocytes, metabolic flux analysis was used to obtain the metabolic profile. We applied a multi-block partial least square (MPLS) model to relate environmental factors and fluxes to levels of intracellular lipids and urea synthesis. The MPLS model identified: (1) the most influential environmental factors and (2) how the metabolic pathways are altered by these factors. Finally, we inverted the MPLS model to determine the concentrations and types of environmental factors required to obtain the most economical solution for achieving optimal levels of cellular function for practical situations.
9764502
On Improved Least Flexibility First Heuristics Superior for Packing and Stock Cutting Problems
Two dimensional cutting and packing problems have applications in many manufacturing and job allocation problems. In particular, in VLSI floor planning problems and stock cutting problems, many simulated annealing and genetic algorithms based methods have been proposed in the last ten years. These researches have mainly been focused on finding efficient data structures for representing packing results so the search space and processing time of the underlying search engine can be minimized. In this paper, we tackle the problem from a different approach. Instead of using stochastic searches, we introduce an effective deterministic optimization algorithm for packing and cutting. By combining an improved Least Flexibility First principle and a greedy search based evaluation routine, we can obtain very encouraging results: In stock cutting problems, our algorithm achieved over 99% average packing density for a series of public rectangle packing data sets, which is significantly better than the 96% packing density obtained by meta-heuristics (simulated annealing) based results while using much less CPU time; whereas in rectangle packing applying the well-known MCNC and GSRC benchmarks, we achieved the best (over 96%) packing density among all known published results packed by other methods. Our encouraging results seem to suggesting a new experimental direction in designing efficient deterministic heuristics for some kind of hard combinatorial problems.
206413943
Test Generation with Dynamic Probe Points in High Observability Testing Environment
High observability testing environment allows internal circuit nodes to be used as test points. However, such flexibility requires the development of new ATPG algorithm. Previous reported algorithm does not guarantee full fault-coverage and assumes all internal circuit nodes are test points. The new algorithm described in this paper will generate a full fault-coverage test set for a fanout free combinational circuit. The main characteristic of the algorithm is that it generates test vectors as well as probe points. As a result, the probe points are different for each test vector, and the number of probe points is the minimum for test set generated. Results obtained show that an average of 30% test vector reduction is achieved compared with the conventional test method which uses only output pins as test points.
7674871
Contextual vector quantization for speech recognition with discrete hidden Markov model
Abstract By using formulation of the finite mixture distribution identification, in this paper, several alternatives to the conventional LBG VQ method are investigated. A contextual VQ method based on the Markov Random Field (MRF) theory is proposed to model the speech feature vector space. Its superiority is confirmed by a series of comparative experiments in a speaker independent isolated word recognition task by using different VQ schemes as the front-end of DHMM. The motivation to use MRF to model the contextual dependence information in the underlying speech production process can be readily extended to acoustic modeling of the basic speech units in speech recognition.