• Annual Conference on Human and Language Technology
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• 1992.10a
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• pp.629-636
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• 1992

컴퓨터의 언어이해 가능성을 반박하는 주된 근거는 형식적 기호들을 처리하는 프로그램이 의미론을 다룰 수 없다는 것이다. 그러나 인과적 계산이론에 따르면 컴퓨터 프로그램이 순전히 구문론적인 것은 아니고 컴퓨터 내부의 기호적 표상의 처리과정에서 의미론적인 지시와 해석이 일어난다고 할 수 있다.

• School Mathematics
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• v.5 no.1
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• pp.59-76
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• 2003

Algebraic signs are important on learning and problem solving of algebra. This study investigated the contents of letters and expressions in textbooks by syntactics, semantics and pragmatics, and considered the introduction and extension processes of algebraic signs didactically. We also categorized the signs, and looked into textbook problems in view of semiotic. The result is that textbook is constructed in syntactics and semantics. Finally, the assessment of 7th grade students' competence in syntactics, semantics, syntactics+- semantics, pragmatics, and problem solving shows that students' ability in syntactics and pragmatics Is a predictive variable for algebraic problem solving.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.33-41
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• 2019

In this paper, we propose a method to learn the redesigned class with background and object for semantic segmentation of indoor scene image. Semantic image segmentation is a technique that divides meaningful parts of an image, such as walls and beds, into pixels. Previous work of semantic image segmentation has proposed methods of learning various object classes of images through neural networks, and it has been pointed out that there is insufficient accuracy compared to long learning time. However, in the problem of separating objects and backgrounds, there is no need to learn various object classes. So we concentrate on separating objects and backgrounds, and propose method to learn after class simplification. The accuracy of the proposed learning method is about 5 ~ 12% higher than the existing methods. In addition, the learning time is reduced by about 14 ~ 60 minutes when the class is configured differently In the same environment, and it shows that it is possible to efficiently learn about the problem of separating the object and the background.

• Korean Journal of Cognitive Science
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• v.2 no.1
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• pp.73-86
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• 1990

This study aims to give a syntactic and semantic analysis of the phe- nomenon of Quantifier Floating in the framework of Generalixed Cate- gorial Grammar. Floated quantifiers like neys-i as in Hakayngtul-i neys-i swul-ul masyessta are syntactically analyxed as VP modifiers(VP/VP), and semantically as involving nominalixed properties. Related forms like neys(NP/NP) and neys-ul(TV-TV) are also given rigorous syntactic and semantic analysis. A successful anaysis sheds light on the possiblity of using Categorial Grammar, which is subject to adjacency principle, for the (computer) processing od Korean.

• Smart Media Journal
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• v.13 no.2
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• pp.52-61
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• 2024

In the shipyard, aerial images are acquired at regular intervals using Unmanned Aerial Vehicles (UAVs) for the management of external storage yards. These images are then investigated by humans to manage the status of the storage yards. This method requires a significant amount of time and manpower especially for large areas. In this paper, we propose an automated management technology based on a semantic segmentation foundation model to address these challenges and accurately assess the status of external storage yards. In addition, as there is insufficient publicly available dataset for external storage yards, we collected a small-scale dataset for external storage yards objects and equipment. Using this dataset, we fine-tune an object detector and extract initial object candidates. They are utilized as prompts for the Segment Anything Model(SAM) to obtain precise semantic segmentation results. Furthermore, to facilitate continuous storage yards dataset collection, we propose a training data generation pipeline using SAM. Our proposed method has achieved 4.00%p higher performance compared to those of previous semantic segmentation methods on average. Specifically, our method has achieved 5.08% higher performance than that of SegFormer.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.147-156
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• 2006

Event logging plays increasingly an important role in system and network management, and syslog is a de-facto standard for logging system events. However, due to the semi-structured features of Common Log Format data most studies on log analysis focus on the frequent patterns. The extensible Markup Language can provide a nice representation scheme for structure and search of formatted data found in syslog messages. However, previous XML-formatted schemes and applications for system logging are not suitable for semantic approach such as ranking based search or similarity measurement for log data. In this paper, based on ranked keyword search techniques over XML document, we propose an XML tree structure through a new data modeling approach for syslog data. Finally, we show suitability of proposed structure for semantic retrieval.

• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.97-105
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• 2004

The reason that an intelligent agent cannot understand the representation of its own perception or activity is caused by the traditional syntactic approach that translates a semantic feature into a simulated string, To implement an autonomously learning intelligent agent, Cohen introduces a experimentally semantic approach that the system learns a contentful representation of physical schema from physically interacting with environment using its own sensors and effectors. We propose that negation is a meta-level schema that enables an agent to recognize its own physical schema, To improve the planner's efficiency, Graphplan introduces the control rule that manipulates the inconsistency between planning operators, but it cannot cognitively understand negation and suffers from redundancy problem. By introducing a negative function not, IPP solves the problem, but its approach is still syntactic and is inefficient in terms of time and space. In this paper, we propose that, to represent a negative fact, a positive atom, which is called opposite concept, is a very efficient technique for implementing an cognitive agent, and demonstrate some empirical results supporting the hypothesis.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.451-456
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• 2017

Semantic segmentation, which is the most basic and complicated problem in computer vision, classifies each pixel of an image into a specific object and performs a task of specifying a label. MRF and CRF, which have been studied in the past, have been studied as effective methods for improving the accuracy of pixel level labeling. In this paper, we propose a semantic partitioning method that combines CNN, a kind of deep running, which is in the spotlight recently, and CRF, a probabilistic model. For learning and performance verification, Pascal VOC 2012 image database was used and the test was performed using arbitrary images not used for learning. As a result of the study, we showed better partitioning performance than existing semantic partitioning algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04a
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• pp.451-453
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• 2000

메시지 순서도(Message Sequence Chart,MSC)는 ITU-T에서 국제적인 표준으로 제안되어 주로 전기 통신 교환 시스템과 같은 실시간 시스템을 위한 통신 행위에 대한 개괄적인 표현 방법으로서 널리 사용되어지고 있으며 요구 명세, 인터페이스 명세, 시뮬레이션 및 검정을 위해 사용되어지고 있다. MSC의 장점이라면 표현된 시스템의 행위를 직관적으로 이해할 수 있게 해주는 그래픽 표현을 제공하는 것이다. 의미론 입장에서 보면 MSC는 폴세스 대수 ACP의 변형인 PA$\varepsilon$에 의해 의미를 부여받고 있긴 하지만 이해하기가 난해한 것이 사실이다. 본 논문에서는 MSC의 동작적 의미를 분석하며 ACSR로 변환하여 그 의미를 보다 쉽게 파악하는 방법론에 대해 다룬다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.263-266
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• 2019

단어 임베딩 모델 중 현재 널리 사용되는 word2vec 모델은 언어의 의미론적 유사성을 잘 반영한다고 알려져 있다. 본 논문은 word2vec 모델로 학습된 단어 벡터가 실제로 의미론적 유사성을 얼마나 잘 반영하는지 확인하는 것을 목표로 한다. 즉, 유사한 범주의 단어들이 벡터 공간상에 가까이 임베딩되는지 그리고 서로 구별되는 범주의 단어들이 뚜렷이 구분되어 임베딩되는지를 확인하는 것이다. 간단한 군집화 알고리즘을 통한 검증의 결과, 상식적인 언어 지식과 달리 특정 범주의 단어들은 임베딩된 벡터 공간에서 뚜렷이 구분되지 않음을 확인했다. 결론적으로, 단어 벡터들의 유사도가 항상 해당 단어들의 의미론적 유사도를 의미하지는 않는다. Word2vec 모델의 결과를 응용하는 향후 연구에서는 이런 한계점에 고려가 요청된다.