• 전자공학회논문지C
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• 제35C권9호
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• pp.21-28
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• 1998

본 논문에서는 회로결선도 분할을 위해 LIME이라는 효과적인 선형배열 알고리즘을 제안한다. LIME은 제안된 비용함수를 이용해 하나의 세그먼트가 남을 때까지 두 개의 세그먼트를 병합한다. 마지막에 남은 하나의 세그먼트가 선형배열에 해당한다. LIME은 회로 결선도의 성긴 특징을 이용하므로 상당히 빠르게 수행된다. 제안된 알고리즘은 기존 방법보다 전형배열을 만드는데 약 8배 빠른 수행 속도를 보이며, 이를 이용한 회로 결선도 분할 결과도 스케일드 비용 면에서 약 17% 향상되었다.

• 한국정보시스템학회지:정보시스템연구
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• 제29권3호
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.256-264
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• 2024

In the last 10 years, AI has made rapid progress, and image classification, in particular, are showing excellent performance based on deep learning. Nevertheless, due to the nature of deep learning represented by a black box, it is difficult to actually use it in critical decision-making situations such as national defense, autonomous driving, medical care, and finance due to the lack of explainability of judgement results. In order to overcome these limitations, in this study, a model description algorithm capable of local interpretation was applied to the inception network-derived AI to analyze what grounds they made when classifying national defense data. Specifically, we conduct a comparative analysis of explainability based on confidence values by performing LIME analysis from the Inception v2_resnet model and verify the similarity between human interpretations and LIME explanations. Furthermore, by comparing the LIME explanation results through the Top1 output results for Inception v3, Inception v2_resnet, and Xception models, we confirm the feasibility of comparing the efficiency and availability of deep learning networks using XAI.

• 산업경영시스템학회지
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• 제45권2호
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• pp.65-76
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• 2022

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

• 한국정보통신학회논문지
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• 제25권12호
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• pp.1784-1789
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• 2021

한국어 감성 분류 작업은 챗봇, 사용자의 물건 구매 평 분석 등 실 서비스에서 사용되고 있으며, 현재 딥러닝 기술의 발달로 높은 성능을 가진 신경망 모델을 활발히 사용하여 감성 분류 작업을 수행하고 있다. 하지만 신경망 모델은 입력 문장이 어떤 단어들로 인해 결과가 예측되었는지 해석하는 것이 쉽지 않으며, 최근 신경망 모델의 해석을 위한 모델 해석 방법들이 활발히 제안되어지고 있다. 본 논문에서는 모델 해석 방법 중 LIME 알고리즘을 이용하여 한국어 감성 분류 데이터 셋으로 학습된 모델들의 입력 문장 내 단어들 중 어떤 단어가 결과에 영향을 미쳤는지 해석하고자 한다. 그 결과, 85.23%의 성능을 보인 양방향 순환 신경망 모델의 해석 결과, 총 25,283개의 긍정, 부정 단어를 포함했지만, 상대적으로 낮은 성능을 보인 84.20%의 Transformer 모델의 해석 결과, 총 26,447개의 긍정, 부정 단어가 포함되어 있어 양방향 순환 신경망 모델보다 Transformer 모델이 신뢰할 수 있는 모델임을 확인할 수 있었다.

• 산업경영시스템학회지
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• 제45권4호
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• pp.86-98
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• 2022

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

• 한국CDE학회논문집
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• 제8권1호
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• pp.10-18
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• 2003

FEM (Finite Element Method) is a fundamental numerical analysis technique in wide spread use in engineering application. As the solving time occupies small portion of entire FEM analysis time because of development of hardware, the relative lime to the whole analysis time to make mesh mod-els is growing. In particular, in the case of stiffeners such as features attached to plate in ship structure, the line constraints are imposed on mesh model together with other constraints such as holes. To auto-matically generate two dimensional quadrilateral mesh with the line constraints, an algorithm is pro-posed based on the constrained Delaunay triangulation and Q-Morph algorithm in which the line constraints are not considered. The performance of the proposed algorithm is evaluated. And some numerical results of our proposed algorithm ate presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.163-165
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• 2002

In this paper a new numerical algorithm for fault distance calculation and arcing fault recognition based on one terminal data and derived in lime domain is presented. The algorithm is derived for the case of most frequent single-phase line to ground fault. The faulted phase voltage at the fault place is modeled as a serial connection of fault resistance and arc voltage. The fault distance and arc voltage amplitude are estimated using Least Error Squares Technique. The algorithm can be applied for distance protection, intelligent autoreclosure and for fault location. The results of algorithm tested through computer simulation are given.

• 인터넷정보학회논문지
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• 제5권5호
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• pp.119-127
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• 2004

언어 기반 프로그래밍 환경의 발전에 따라 점진적 파싱에 대한 연구는 핵심적인 분야가 되었다. 본 논문의 목적은 파싱 속도(lime)와 기억장소가 많이 요구하는 기존의 알고리즘들보다 효율적인 점진적 파싱 알고리즘을 제시하는데 있다. 본 논문에서는 스택을 이용하지 않는 스레드 트리 구성 알고리즘을 제안하였다. 또한 노드의 재파싱 과정을 없애기 위해 스택을 이용하지 않는 노드 생성 알고리즘과 점진적 스레드 트리 구성 알고리즘을 제안하였다.

• 제어로봇시스템학회논문지
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• 제7권1호
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• pp.1155-1161
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• 2001

We propose an adaptive nonlinear control algorithm for high precision tracking and stabilization of LOS(Line-of-Sight). The friction parameters of the LOS gimbal are estimated by off-line evolutionary strategy and the friction is compensated by estimated friction compensator. Especially, as the nonlinear control input in a small tracking error zone is enlarged by the nonlinear function, the steady state error is significantly reduced. The proposed algorithm is a direct adaptive control method based on the Lyapunov stability theory, and its convergence is guaranteed under the limited modeling error or torque disturbance. The performance of the pro-posed algorithm is verified by computer simulation on the LOS gimbal model of a moving vehicle.