• 수완나부미
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• 제10권2호
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• pp.181-213
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• 2018

How to study Southeast Asia (SEA)? The need to explore and identify methodologies for studying SEA are inherent in its multifaceted subject matter. At a minimum, the region's rich cultural diversity inhibits both the articulation of decisive defining characteristics and the training of scholars who can write with confidence beyond their specialisms. Consequently, the challenges of understanding the region remain and a consensus regarding the most effective approaches to studying its history, identity and future seem quite unlikely. Furthermore, "Area Studies" more generally, has proved to be a less attractive frame of reference for burgeoning scholarly trends. This paper will propose a new tool to help address these challenges. Even though the science of artificial intelligence (AI) is in its infancy, it has already yielded new approaches to many commercial, scientific and humanistic questions. At this point, AI has been used to produce news, generate better smart phones, deliver more entertainment choices, analyze earthquakes and write fiction. The time has come to explore the possibility that AI can be put at the service of the study of SEA. The paper intends to lay out what would be required to develop SEABOT. This instrument might exist as a robot on the web which might be called upon to make the study of SEA both broader and more comprehensive. The discussion will explore the financial resources, ownership and timeline needed to make SEABOT go from an idea to a reality. SEABOT would draw upon artificial neural networks (ANNs) to mine the region's "Big Data", while synthesizing the information to form new and useful perspectives on SEA. Overcoming significant language issues, applying multidisciplinary methods and drawing upon new yields of information should produce new questions and ways to conceptualize SEA. SEABOT could lead to findings which might not otherwise be achieved. SEABOT's work might well produce outcomes which could open up solutions to immediate regional problems, provide ASEAN planners with new resources and make it possible to eventually define and capitalize on SEA's "soft power". That is, new findings should provide the basis for ASEAN diplomats and policy-makers to develop new modalities of cultural diplomacy and improved governance. Last, SEABOT might also open up avenues to tell the SEA story in new distinctive ways. SEABOT is seen as a heuristic device to explore the results which this instrument might yield. More important the discussion will also raise the possibility that an AI-driven perspective on SEA may prove to be even more problematic than it is beneficial.

• 대한원격탐사학회지
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• 제39권6_1호
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• pp.1437-1449
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• 2023

본 연구의 목적은 도시지역의 투수면적 및 불투수면적이 수목의 생장에 영향을 미치는지 분석하는 것이다. 이를 위해 동일한 시기에 식재한 나무 6종의 생장 차이를 알아보기 위해 Sentinel-2를 이용하여 제작한 Normalised Difference Vegetation Index (NDVI)와 환경부의 세분류 토지피복지도를 이용하여 투수면적과 불투수면적이 수목 생장에 미치는 영향을 분석했다. 이를 위해 Geospatial eXplainable Artificial Intelligence (GeoXAI) 개념을 적용했다. 분석결과 소나무는 10 m 범위, 느티나무, 메타세콰이어, 은행나무는 20 m 범위, 양버즘나무는 30 m 범위, 왕벚나무는 40 m 범위에 포함된 토지피복들의 면적을 고려했을때 모델의 설명력이 가장 우수한 것으로 나타났다. 그리고 투수 면적이 넓을수록 수목의 생장이 활발하다는 양의 상관관계를 나타내었으며, 주변에 인공지반과 같이 불투수 면적이 넓을 경우 수목의 생장과 음의 상관관계를 보였다. 이는 수목의 생장에 있어 주변의 투수 및 불투수 면적이 수목의 생장에 영향을 미침을 알 수 있었으며, 수종에 따라 영향을 미치는 범위 또한 다르다는 것을 알 수 있었다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Computers and Concrete
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• 제22권4호
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• pp.419-437
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• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.I
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• pp.1-30
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• 2000

고차원의 식물공장 개발이 논의되었는데, 이것은 식물생장환경을 위한 공정제어, 물류 조작을 위한 기계화, 생산을 위한 시스템 제어, 그리고 컴퓨터 응용과 같은 기술들을 포함한다. 더 나아가 식물공장의 장점들은 생산의 안정화, 높은 생산 효율, 생장기간의 단축화를 통한 생산물의 더 나은 품질관리, 더 좋은 조건들, 낮은 소요 노동력, 그리고 산업적 개념의 더 용이한 적용을 포함한다. 마지막으로 태양광과 인공광을 사용하는 궁극적이 식물공장을 실현하기 위하여, 제어공학의 지능적 접근, 생리학적 생태환경과 인공지능(AI)이 필연적이어서 저자가 이룩한 약간의 연구업적에 근거하여 소개되었다.

• 한국전자통신학회논문지
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• 제14권6호
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• pp.1181-1186
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• 2019

최근에 패스트푸드점, 개인이 운영하는 소규모 식당이나 카페 등에서도 키오스크를 이용하여 주문하고 결제하는 모습들을 흔하게 볼 수 있다. 팔을 사용하는데 불편한 장애인과 휠체어에 앉아 있는 장애인들은 그래픽 버튼을 눌러 사용하기가 어렵고, 노인들은 나이가 많아질수록 새로운 정보를 받아들이는 인지능력이 떨어져 사용하기에 불편함을 느낀다. 본 논문에서는 이러한 문제점을 보완하기 위해 사용자가 키오스크와 상호작용을 할 때 기본적으로 제공되는 시각적인 명령요소에 인공지능 스피커의 음성적 명령요소를 추가하여 키오스크에서 음성으로 명령을 수행할 수 있는 주문결제 시스템을 설계하고 구현한다.

• 지능정보연구
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• 제7권2호
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• pp.19-33
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• 2001

본 연구에서는 부도예측용 인공신경망의 입력노드 선정을 위한 휴리스틱으로 연결강도분석접근법을 제안한다. 연결강도분석은 학습이 끝난 인공신경망에서 입력노드와 은닉노드를 연결하는 연결가중치의 절대값 즉, 연결강도를 분석하여 입력변수를 선정하는 접근법으로, 선정기준에 따라 약체연결뉴론제거법과 강체연결뉴론선택법을 들 수 있다. 본 연구에서는 약체연결뉴론제거법, 강체연결뉴론선택법 그리고 이 두 기법을 통합한 통합 연결강도 모형을 제안하여 각각 의사결정트리 및 다변량판별분석에 의해 선정된 입력변수를 이용한 인공신경망 모형과 예측율을 비교한다. 실험 결과 본 연구에서 제안하고 있는 방법론이 의사결정트리나 다변량판별분석 기법보다 높은 예측율을 보여주었다. 특히 두 기법의 통합연결강도 모형의 경우에는 다른 단일 기법보다 높은 예측율을 보이고 있다.

• Journal of Welding and Joining
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• 제10권4호
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• pp.199-212
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• 1992

Expert system is a practical application part of the artificial intelligence and can be generally described as a computer-based system designed to simulate the knowledge and reasoning of a human expert, and to make that knowledge conveniently available to other people in a useful way. Expert systems consist of three major components, knowledge base, inference engine and user interface. In this paper, it is aimed to construct a prototype system to design the horizontal-typed pressure vessel. To do this, a representative artificial programming language, Turbo Prolog, was employed, and the knowledge representation was mainly done by the production rule such as "If(condition), than (action)" style and by the predicate logic. In the developed system, it was quite easy to represent the knowledge of "If(condition), then (action)"style and by the predicate logic. In the developed system, it was quite easy to represent the knowledge of "If(condition). then(action)" style and the various table-like data. It was also effective to represent the graphics. Though this expert system is by now small and incomplete, it is possible to expand it to a larger and refined system later.rger and refined system later.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.