• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 하계학술대회
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• pp.1268-1271
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• 2022

Since it is common to have touch-screen devices, it is less challenging to draw sketches anywhere and save them in vector form. Current research on sketches considers coordinate sequence data and adopts sequential models for learning sketch representation in sketch understanding. In the sketch dataset, it has become customary that the dataset is in vector coordinate format. Moreover, the popular dataset does not consider real-life sketches, sketches from pencil, pen, and paper. Art psychology uses real-life sketches to analyze patients. ETRI presents a unique sketch dataset for sketch recognition of autism spectrum disorder in pixel format. We present a method to formulate the dataset for better generalization of sketch data. Through experiments, we show that pixel-based models can produce a good performance.

• Kyungpook Mathematical Journal
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• 제48권4호
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• pp.559-575
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• 2008

A commutative hypercomplex system $L_1$(Q,m) is, roughly speaking, a space which is defined by a structure measure (c(A,B, r), (A,$B{\in}{\beta}$(Q)). Such space has bee studied by Berezanskii and Krein. Our main purpose is to establish a generalization of convolution semigroups and to discuss the role of the L$\'{e}$vy measure in the L$\'{e}$vy-Khinchin representation in terms of continuous negative definite functions on the dual hypercomplex system.

• 대한수학회지
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• 제53권2호
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• pp.347-362
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• 2016

In this paper, we are concerned with abstract random linear operators on probabilistic unitary spaces which are a generalization of generalized random linear operators on a Hilbert space defined in [25]. The representation theorem for abstract random bounded linear operators and some results on the adjoint of abstract random linear operators are given.

• 한국경영과학회지
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• 제23권2호
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• pp.131-156
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• 1998

Cooperative query answering is a research effort to develop a fault-tolerant and intelligent database system using the semantic knowledge base constructed from the underlying database. Such knowledge base has two aspects of usage. One is supporting the cooperative query answering Process for providing both an exact answer and neighborhood information relevant to a query. The other is supporting ongoing maintenance of the knowledge base for accommodating the changes in the knowledge content and database usage purpose. Existing studies have mostly focused on the cooperative query answering process but paid little attention on the dynamic knowledge base maintenance. This paper proposes a multi-level knowledge representation framework called Knowledge Abstraction Hierarchy (KAH) that can not only support cooperative query answering but also permit dynamic knowledge maintenance. The KAH consists of two types of knowledge abstraction hierarchies. The value abstraction hierarchy is constructed by abstract values that are hierarchically derived from specific data values in the underlying database on the basis of generalization and specialization relationships. The domain abstraction hierarchy is built on the various domains of the data values and incorporates the classification relationship between super-domains and sub-domains. On the basis of the KAH, a knowledge abstraction database is constructed on the relational data model and accommodates diverse knowledge maintenance needs and flexibly facilitates cooperative query answering. In terms of the knowledge maintenance, database operations are discussed for the cases where either the internal contents for a given KAH change or the structures of the KAH itself change. In terms of cooperative query answering, database operations are discussed for both the generalization and specialization Processes, and the conceptual query handling. A prototype system has been implemented at KAIST that demonstrates the usefulness of KAH in ordinary database application systems.

• 한국컴퓨터정보학회논문지
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• 제27권2호
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• pp.43-51
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• 2022

이 논문은 문법적 추론에서 유전자 알고리즘의 진화대상으로 테이블 표현(Tabular representation: TBL)을 이용한 문맥자유 문법(Context-free grammar: CFG)을 학습하는 기존의 방법을 개선하여 더 효율적인 결과를 얻은 그 방법과 실험 결과를 제시한다. 이 논문에서 소개하는 개선된 점은 두가지로, 첫째는 적합도 함수를 긍정과 부정의 예들에 대한 학습 평가를 동시에 반영하도록 수식을 개선하고 둘째는 긍정적 학습 예들로부터 생성된 TBL들에 대응되는 파티션(partition)들을 학습 문자열의 크기별로 분류하여 부류별 진화 과정을 진행하며 그 성공률에 따라 구성 비율을 조정하여 다음세대에 생존에 연계하는 학습 방법을 적용한다. 이 개선점들은 학습 예들의 크기에 따른 TBL의 크기가 여러 개체들 사이의 교배와 일반화 단계에서 복잡성과 어려움을 해결하여 기존 방법보다도 좋은 효율을 제공한다. 이 연구는 기존 방법에서 제안된 언어들로 실험하고 그 결과는 기존 방법보다 같은 성공률을 갖는 상태에서 학습 완성의 평균 세대수가 적게 걸리는 다소 빠른 세대속도의 결과를 보여준다. 앞으로 이 방법은 확장된(extended) CYK에 시도할 수 있으며 더 나아가 좀 더 복잡한 파싱 테이블(parsing table)에도 적용할 가능성을 제시한다.

• Smart Structures and Systems
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• 제30권3호
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.785-804
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• 2017

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• 대한수학교육학회지:수학교육학연구
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• 제24권2호
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• pp.205-225
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• 2014

본 연구는 초등학교 6학년 학생들이 기하 증가 패턴을 탐구하는 상황에서 함수적 관계를 어떻게 인식하고 일반화하며 표현하는지에 대해 분석하였다. 연구 결과, 처음에는 학생들이 그림에 의존하여 문제를 해결하는 경향을 보였으나, 후속 항들을 탐구하는 과정에서 일반화에 대한 시도가 자연스럽게 나타났다. 또한, 패턴 탐구의 결과를 어떤 방식으로 표현하는지는 개인에 따라 차이가 있었는데, 이 표현 방식은 패턴을 일반화하고 유사 상황에 적용하는 과정에도 영향을 끼쳤다. 본 연구는 이러한 결과들을 토대로, 초등학교에서의 함수적 사고의 지도 방안에 대한 시사점을 제공한다.

• 한국경영과학회지
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• 제24권1호
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• pp.99-117
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• 1999

Since query language is used as a handy tool to obtain information from a database, a more intelligent query answering system is needed to provide user-friendly and fault-tolerant human-machine Interface. Frequently, database users prefer less rigid querying structure, one which allows for vagueness in composing queries, and want the system to understand the intent behind a query. When there is no matching data available, users would rather receive approximate answers than a null information response. This paper presents a knowledge abstraction database that facilitates the development of such a fault-tolerant and intelligent database system. The proposed knowledge abstraction database adepts a multilevel knowledge representation scheme called the knowledge abstraction hierarchy(KAH), extracts semantic data relationships from the underlying database, and provides query transformation mechanisms using query generalization and specialization steps. In cooperation with the underlying database, the knowledge abstraction database accepts vague queries and allows users to pose approximate queries as well as conceptually abstract queries. Specifically. four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection, and conceptual Join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database application systems.