Young S. Han

Automatic thesaurus construction using Bayesian networks

Automatic thesaurus construction is accomplished by extracting term relations mechanically. A popular method uses statistical anaJysis to discover the term relations. For low frequency terms, however, the statistical information of the terms cannot be reliably used for deciding the relationship of terms. This problem is generally referred as data sparseness problem. Unfortunately, many studies have been said that the low frequency terms are of most use in information retrieval. In this paper, we formalize the statistical behaviors of terms by using inference network. A formal approach for the data sparseness problem which is crucial in constructing a thesaurus is developed. The validity of this approach is shown by experiments.

Microstructural changes during superplastic deformation of Fe-24Cr-7Ni-3Mo-0.14N duplex stainless steel

The superplasticity of Fe‐24Cr‐7Ni‐3Mo‐0.14N duplex stainless steel after solution treatment at 1350°C followed by 90% cold rolling was investigated at 850°C with a strain rate ranging from 10 3 to 10 1 s 1 . The microstructure of duplex stainless steel consists of matrix g phase having low angle boundaries and s phase as second phase particles before the deformation at 850°C. The constituent phases in the duplex stainless steel were found to be changed following aiagiagsigs through phase transformation during the deformation at 850°C. A maximum elongation of 750% was obtained at 850°C with strain rate of 3.1610 3 s 1 . The low angle grain boundaries were changed into high angle grain boundaries by dynamic recrystallization of g phase at an early stage of deformation. The dislocation density within matrix g grains was low and a significant strain-induced grain growth was observed during the deformation. The misorientation angles between the neighboring g grains increased with increasing strain; thus the low angle grain boundaries were transformed into high angle grain boundaries suitable for sliding by the dynamic recrystallization during the deformation. The grain boundary sliding assisted by dynamic recrystallization is considered a controlling mechanism for superplastic deformation at 850°C.

The effects of deformation twins and strain-induced ϵ-martensite on mechanical properties of an Fe-32Mn-12Cr-0.4C cryogenic alloy

In this research, the tensile properties of a Fe-32Mn-12Cr-0.4C cryogenic alloy were investigated at temperatures between {minus}196 C to 250 C. This study was intended to clarify the influence of temperature on tensile properties and to correlate the deformed microstructures with the tensile elongation. The formation of strain-induced changes, deformation twins and strain-induced {var_epsilon}-martensite, was analyzed at various temperatures. The effects of the strain-induced changes on strain hardening rate were discussed to explain the variation of tensile elongation of Fe-32Mn-12Cr-0.4C cryogenic alloy.

The effect of Al on mechanical properties and microstructures of Fe-32Mn-12Cr-xAl-0.4C cryogenic alloys

Abstract The effect of Al addition (0–3 wt%) on tensile properties and microstructures of Fe-32Mn-12Cr- x Al-0.4C cryogenic alloys were investigated at temperatures ranging from −196 to 250 °C. The Al is an austenite stabilizer suppressing the formation of the strain induced ɛ -martensite, while it behaved as a δ -ferrite stabilizer when added to 3 wt%. The Fe-32Mn-12Cr-0Al-0.4C alloy showed the strain induced ɛ -martensite after tensile deformation at −196 °C. Deformation twins were observed in deformed cryogenic alloys except Fe-32Mn-12Cr-2Al-0.4C alloys at temperatures ranging from −196 to 25 °C. The formation of both the deformation twins and the strain induced ɛ -martensite enhanced the tensile elongation by retarding local necking. The Fe-32Mn-12Cr-(0,1,3)Al-0.4C alloys exhibited elongation peaks within a temperature range forming the deformation twins. As the stacking fault energy increases with increasing Al content, the elongation peak shifted to lower temperature with increasing Al content in Fe-32Mn-12Cr- x Al-0.4C alloy. The continuous formation of strain induced phases, deformation twins and ɛ -martensite, up to a large strain was important to increase the tensile elongation of Fe-32Mn-12Cr- x Al-0.4C cryogenic alloys.

Restricted representation of phrase structure grammar for building a tree annotated corpus of Korean

In this paper, we introduce a method to represent phrase structure grammars for building a large annotated corpus of Korean syntactic trees. Korean is different from English in word order and word compositions. As a result of our study, it turned out that the differences are significant enough to induce meaningful changes in the tree annotation scheme for Korean with respect to the schemes for English. A tree annotation scheme defines the grammar formalism to be assumed, categories to be used, and rules to determine correct parses for unsettled issues in parse construction. Korean is partially free in word order and the essential components such as subjects and objects of a sentence can be omitted with greater freedom than in English. We propose a restricted representation of phrase structure grammar to handle the characteristics of Korean more efficiently. The proposed representation is shown by means of an extensive experiment to gain improvements in parsing time as well as grammar size. We also describe the system named Teb that is a software environment set up with a goal to build a tree annotated corpus of Korean containing more than one million units.

The effects of thermo-mechanical treatments on superplasticity of Fe-24Cr-7Ni-3Mo-0.14N duplex stainless steel

In this study, the effect of thermo-mechanical treatment on superplasticity of 24Cr-7Ni-3Mo-0.14N alloy was investigated at 850 C in three phase regime consisting of {alpha}, {gamma} and {sigma} phases. In order to examine the effect of thermo-mechanical treatment conditions on superplasticity, the solution treatment temperature and cold reduction ratio were varied. The effects of thermo-mechanical treatment conditions on the microstructural factors of constituent phases were analyzed. The relationships between the microstructural factors and superplasticity in Fe-24Cr-7Ni-3Mo-0.14N duplex stainless steel were discussed.

A probabilistic approach to compound noun indexing in Korean texts

In this paper we address the problem of compound noun indexing that is about segmenting or decomposing compound nouns into promising index terms. Compound nouns as index terms that usually subscribe to specific notions tend to increase the precision of retrieval performance. The use of the component nouns of a compound noun as index terms, on the other hand, may improve the recall performance, but can decrease the precision.Our proposed method to handle compound nouns with a goal to increase the recall while preserving the precision computes the relevance of the component nouns of a compound noun to the document content by comparing the document sets that are supported by the component nouns and the terms of the document. The operational content of a term is represented as the probabilistic distribution of the term over the document set.Experiments with a set of 1,000 documents show that our method gains 33% increase of retrieval performance compared to the indexing method without compound noun analysis, and is as good as manual decomposition by human experts.

Phenomena and mechanism on superplasticity of duplex stainless steels

The superplasticity of Fe-24Cr-7Ni-3Mo-0.14N duplex stainless steel after being solution treated at 1350°C followed by 90% cold rolling was investigated at 850°C with a strain rate ranging from 10-3-10-1s-1. The microstructure of duplex stainless steel consists of a matrix γ phase having low angle grain boundaries and a σ phase as second phase particles before the deformation at 850°C. It is well known that the constituent phases in duplex stainless steel is changed following α→α+γ→α+γ+σ→γ+σ through phase transformation during deformation at 850°C. The final microstructure of duplex stainless steel consisted of 70 vol.% of γ and 30 vol.% of the σ phase. A maximum elongation of 750% was obtained at 850°C with a strain rate of 3.16xl0-3s-1. The dislocation density within matrix γ grains was low and a significant strain-induced grain growth was observed during the deformation. The misorientation angles between the neighboring γ grains increased as the strain increased, thus the low angle grain boundaries were transformed into high angle grain boundaries suitable for sliding by dynamic recrystallization during the deformation at 850°C. The grain boundary sliding assisted by dynamic recrystallization is considered to be a controlling mechanism for superplastic deformation at 850°C.

Collocation map for overcoming data sparseness

Statistical language models are useful because they can provide probabilistic information upon uncertain decision making. The most common statistic is n-grams measuring word cooccurrences in texts. The method suffers from data shortage problem, however. In this paper, we suggest Bayesian networks be used in approximating the statistics of insufficient occurrences and of those that do not occur in the sample texts with graceful degradation. Collocation map is a sigmoid belief network that can be constructed from bigrams. We compared the conditional probabilities and mutual information computed from bigrams and Collocation map. The results show that the variance of the values from Collocation map is smaller than that from frequency measure for the infrequent pairs by 48%. The predictive power of Collocation map for arbitrary associations not observed from sample texts is also demonstrated.

Efficient inferencing for sigmoid Bayesian networks by reducing sampling space

A sigmoid Bayesian network is a Bayesian network in which a conditional probability is a sigmoid function of the weights of relevant arcs. Its application domain includes that of Boltzmann machine as well as traditional decision problems. In this paper we show that the node reduction method that is an inferencing algorithm for general Bayesian networks can also be used on sigmoid Bayesian networks, and we propose a hybrid inferencing method combining the node reduction and Gibbs sampling. The time efficiency of sampling after node reduction is demonstrated through experiments. The results of this paper bring sigmoid Bayesian networks closer to large scale applications.