Repair of Cracked Aluminum Overhead Sign Structures with Glass Fiber Reinforced Polymer Composites

Transportation departments have been using aluminum overhead sign structures since the 1950s. It is well documented that cracks develop in the welds between diagonal and chord members due to fatigue stresses from wind-induced vibration of the slender members. The cracks propagate to complete failure of the members, which can cause collapse of the truss and inflict injuries. The original design of overhead sign structures did not consider fatigue as a limit state. In addition, field welding of aluminum structures for any possible repairs is prohibited. A repair method for the cracked aluminum welded connections between diagonals and chord members using glass fiber reinforced polymer composites (GFRPs) is proposed. The static load carrying capacity of the welded connection, and the cracked connection repaired with GFRP composites are established. The paper describes the surface preparation of the aluminum tubular members, and the architecture and application sequence of the GFRP composite to retrofit the connection. Experimental results are presented from static tests of welded aluminum connections, welded aluminum connections retrofitted with GFRP composites, and new aluminum connections that depend only on GFRP composite elements for their strength. The results from monotonic static tests carried out on cracked welded specimens from actual sign structures show that the retrofitted connection with GFRP reinforcement achieved 1.17 to 1.25 times the capacity of the welded aluminum connection without any visible cracks. This result, and the minimal traffic disruption anticipated in the actual field application, makes this retrofit method a good candidate for implementation.     

SignMT: An alternative language learning tool

Learning a second language is very difficult, especially, for the disabled; the disability may be a barrier to learn and to utilize information written in text form. We present the SignMT, Thai sign to Thai machine translation system, which is able to translate from Thai sign language into Thai text. In the translation process, SignMT takes into account the differences between Thai and Thai sign language in terms of both syntax and semantic to ensure the accuracy of translation. SignMT was designed to be not only an automatic interpreter but also a language learning tool. It provides meaning of each word in both text and image forms which is easy to understand by the deaf. The grammar information and the order of the sentence are presented in order to help the deaf in learning Thai, their second language. With SignMT, deaf students are less dependent on a teacher, have more freedom to experiment with their own language, and improve their knowledge and learning skill.     

Probability-Based Chinese Text Processing and Retrieval

We discuss the use of probability-based natural language processing for Chinese text retrieval. We focus on comparing different text extraction methods and probabilistic weighting methods. Several document processing methods and probabilistic weighting functions are presented. A number of experiments have been conducted on large standard text collections. We present the experimental results that compare a word-based text processing method with a character-based method. The experimental results also compare a number of term-weighting functions including both single-unit weighting and compound-unit weighting functions.     

Adaptable learning assistant for item bank management

We present PKIP, an adaptable learning assistant tool for managing question items in item banks. PKIP is not only able to automatically assist educational users to categorize the question items into predefined categories by their contents but also to correctly retrieve the items by specifying the category and/or the difficulty level. PKIP adapts the “categorization learning model” to improve the system’s categorization performance using the incoming question items. PKIP tool has an advantage over the traditional document categorization methods in that it can correctly categorize the question item which lacks keywords since it adopts the feature selection technique and support vector machine approach to item bank text categorization. In our initial experimentation, PKIP was designed and implemented to manage the Thai high primary mathematics question items. PKIP was tested and evaluated in terms of both system accuracy and user satisfaction. The evaluation result shows that the system accuracy is acceptable and PKIP satisfies the need of the users.     

A framework for computing extrasentential references

We are concerned with developing a computational method for selecting possible antecedents of referring expressions over sentence boundaries. Our stratified model which uses a Λ-categorial language for meaning representation incorporates valuable features of Fregean-type semantics (a la Lewis, Montague, Partee, and others) along with features of situation semantics developed by Barwise and Perry. We consider a series of selected two-sentence stories which we use to illustrate referential interdependencies between sentences. We explain the conditions under which such dependencies arise, explain the conditions under which various translations can be performed, and formalize a set of rules which specify how to compute the reference. We restrict our discussion to two-sentence stories to avoid most of the problems inherent in where to look for the reference, that is, how to determine the proper antecedent. We restrict our considerations in this paper to situations where a reference, if it can be computed at all, has a unique antecedent. Thus we consider examples such as John wants to catch a fish. He (John) wants to eat it. and John interviewed a man. The man killed him (John). We then summarize the transformation which encompasses these rules and relate it to the stratified model. We discuss three aspects of this transformation that merit special attention from the computational viewpoint and summarize the contributions we have made. We also discuss the computational characteristics of the stratified model in general and present our ideas for a computer realization; there is no implementation of the _t"ratified model at this time.     

Data-driven discovery of quantitative rules in relational databases

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases     

Providing quality responses with natural language interfaces: thenull value problem

An underlying relational database model and the database query language SQL are assumed, and methods are presented for responding with appropriate answers to null value responses. This is done by using a knowledge base based on RM/T, an extended relational model. The advantages of this approach are described. To demonstrate the utility of the knowledge base model, a simple knowledge base is constructed. The algorithms that provide additional information when a null answer is returned are detailed     

Data Mining in Large Databases Using Domain Generalization Graphs

Attribute-oriented generalization summarizes the information in a relational database by repeatedly replacing specific attribute values with more general concepts according to user-defined concept hierarchies. We introduce domain generalization graphs for controlling the generalization of a set of attributes and show how they are constructed. We then present serial and parallel versions of the Multi-Attribute Generalization algorithm for traversing the generalization state space described by joining the domain generalization graphs for multiple attributes. Based upon a generate-and-test approach, the algorithm generates all possible summaries consistent with the domain generalization graphs. Our experimental results show that significant speedups are possible by partitioning path combinations from the DGGs across multiple processors. We also rank the interestingness of the resulting summaries using measures based upon variance and relative entropy. Our experimental results also show that these measures provide an effective basis for analyzing summary data generated from relational databases. Variance appears more useful because it tends to rank the less complex summaries (i.e., those with few attributes and/or tuples) as more interesting.