• Journal of Korea Society of Industrial Information Systems
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• v.4 no.3
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• pp.1-7
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• 1999

An important task for any software project manager is to be able to predict and control project size. Unfortunately, there is comparatively little work that deals with the problem of building prediction methods for software size in fourth-generation languages and database projects. In this paper, we propose a new estimation method for estimating for software size based on minimum relative error(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the observed measures which can be obtained early in the development life cycle. In order to verify the performance of the proposed estimation method for software size in terms of both quality of fit and predictive quality, the experiments has been conducted for the dataset Ⅰ and Ⅱ, respectively. For the data set Ⅰ and Ⅱ, our proposed prediction method was shown to be superior to the traditional method LS and RLS in terms of both the quality of fit and predictive quality when applied to data obtained from actual software development projects.

• Annual Conference of KIPS
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• 2021.05a
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• pp.501-504
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• 2021

한국어 심층학습 언어모델인 KoBERT와, 확률적 기계학습 분류기인 나이브 베이즈와 로지스틱 회귀를 이용하여 유학생이 작성한 한국어 쓰기 답안지의 점수 구간을 예측하는 실험을 진행하였다. 네가지 주제('직업', '행복', '경제', '성공')를 다룬 답안지와 점수 레이블(A, B, C, D)로 쌍을 이룬 학습데이터 총 304건으로 다양한 자동분류 모델을 구축하여 7-겹 교차검증을 시행한 결과 KoBERT가 나이브 베이즈나 로지스틱 회귀보다 약간 우세한 성능을 보였다.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.97-105
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• 1999

An important task for any software project manager is to be able to predict and control project size. Unfortunately, there is comparatively little work that deals with the problem of building estimation methods for software size in fourth-generation languages systems. In this paper, we propose a new estimation method for estimating for software size based on minimum relative error(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the observed measures which can be obtained early in the development life cycle. In order to verify the performance of the proposed estimation method for software size in terms of both quality of fit and predictive quality, the experiments has been conducted for the dataset I and II, respectively. For the data set I and II, our proposed estimation method was shown to be superior to the traditional method LS and RLS in terms of both the quality of fit and predictive quality when applied to data obtained from actual software development projects.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.554-561
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• 2008

Prediction technique of user's intention can be used as a post-processing method for reducing the search space of an automatic speech recognizer. Prediction technique of system's intention can be used as a pre-processing method for generating a flexible sentence. To satisfy these practical needs, we propose a statistical model to predict speakers' intentions that are generalized into pairs of a speech act and a concept sequence. Contrary to the previous model using simple n-gram statistic of speech acts, the proposed model represents a dialogue history of a current utterance to a feature set with various linguistic levels (i.e. n-grams of speech act and a concept sequence pairs, clue words, and state information of a domain frame). Then, the proposed model predicts the intention of the next utterance by using the feature set as inputs of CRFs (Conditional Random Fields). In the experiment in a schedule management domain, The proposed model showed the precision of 76.25% on prediction of user's speech act and the precision of 64.21% on prediction of user's concept sequence. The proposed model also showed the precision of 88.11% on prediction of system's speech act and the Precision of 87.19% on prediction of system's concept sequence. In addition, the proposed model showed 29.32% higher average precision than the previous model.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.385-390
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• 2022

관계추출은 문서 혹은 문장에서 자동으로 엔티티들간의 관계를 추출하는 기술로, 비정형 데이터를 정형데이터로 변환하기에 자연어 처리 중에서도 중요한 분야중 하나이다. 그 중에서도 대화 관계추출은 기존의 문장 단위의 관계추출과는 다르게 긴 길이에 비해 적은 정보의 양, 빈번하게 등장하는 지시대명사 등의 특징을 가지고 있어 주어와 목적어 사이의 관계를 예측하기에 어려움이 있었다. 본 연구에서는 이러한 어려움을 극복하기 위해 대화의 특성을 고려한 대화 그래프를 구축하고 이를 이용한 모델을 제안한다. 제안하는 모델은 상호참조 정보와 문맥정보를 더 반영한 그래프를 통해 산발적으로 퍼져있는 정보를 효율적으로 수집하고, 지시대명사로 인해 어려워진 중요 발화 파악 능력을 증진시켰다. 또한 이를 실험적으로 보이기 위하여 대화 관계추출 데이터셋에 실험해본 결과, 기존 베이스라인 보다 약 10 % 이상의 높은 F1점수를 달성하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.11a
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• pp.1-6
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• 2002

To provide an easy operation of drift prediction model in SAR(search and rescue) mission a GUI program running on Window environment has developed. Users can make choice of input data on the screen by just clicking the mouse and the prediction results of datum points and trajectories of vessels are drawn on the map. The program contains both Leeway Equation model and mathematical model. The FORTRAN language was used in programming and Lehay Winteracter 4.0 software was utilized for graphic presentation. The result of May, 2001 Busan field experiment was plotted with that of model prediction for demonstration purpose.

• Journal of Navigation and Port Research
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• v.26 no.5
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• pp.491-495
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• 2002

To provide an easy operation of drift prediction model in SAR(search and rescue) mission a GUI program running on Windows environment has developed. Users can make choice of input data on the screen by just clicking the mouse and the prediction results of datum points and trajectories of vessels are drawn on the electric chart. The program contains both Leeway Equation model and Mathematical model. The FORTRAN language was used in programming and Lehay Winteraction 4.0 software was utilized for graphic presentation. The result of May, 2001 Busan field experiment was plotted with that of model prediction for demonstration purpose.

• New Physics: Sae Mulli
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• v.68 no.12
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• pp.1364-1373
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• 2018

A recognizable form having meaning is called a sign in semiotics. The sign is transformed into a physical counter form in this work. Its internal structure is restricted on the linguistic concept structure. We borrow the concept of a mathematical function from the utility function of a rational personal in the economy. Universalizing the utility function by introducing the consistency of independency on the manner of construction, we construct the probability. We introduce a random variable for the probability and join it to a position variable. Thus, we propose a physical sign and its serial changes in the forms of stochastic equations. The equations estimate three patterns (jumping, drifting, diffusing) of possible solutions, and we find them in the one-day stock-price flow. The periods of jumping, drifting and diffusing were about 2, 3.5, and 6 minutes for the Kia stock on 11/05/2014. Also, the semiotic sign (icon, index, symbol) can be expected from the equations.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.82-88
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• 2024

As the modern maritime industry rapidly progresses through technological advancements, data processing technology is emphasized as a key driver of this development. Natural language processing is a technology that enables machines to understand and process human language. Through this methodology, we aim to develop a model that predicts the proportions of outcomes when entering new written judgments by analyzing the rulings of the Marine Safety Tribunal and learning the cause-providing ratios of previously adjudicated ship collisions. The model calculated the cause-providing ratios of the accident using the navigation applied at the time of the accident and the weight of key keywords that affect the cause-providing ratios. Through this, the accuracy of the developed model could be analyzed, the practical applicability of the model could be reviewed, and it could be used to prevent the recurrence of collisions and resolve disputes between parties involved in marine accidents.