• Environmental Engineering Research
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• 제14권2호
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• 한국멀티미디어학회논문지
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• 제10권5호
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• pp.631-641
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• 2007

국내 게임시장은 MMORPG만을 생산하는 기이한 형태로 발전하고 있다. 하지만 지능형 캐릭터의 수준은 여전히 제자리걸음을 하고 있다. 본 논문에서는 유전자 알고리즘과 신경망을 사용하여 보다 뛰어난 지능을 가진 캐릭터 구현 방안을 제시하고자 한다. 또한 현재 MMORPG에서 사용되는 다른 인공지능 기술들과 비교했을 때, 그 성능이 뒤쳐지지 않음을 증명하고, 실제 MMORPG에 적용할 수 있는 구체적인 알고리즘과 구현 방법에 대해 설명한다.

• 전기학회논문지
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• 제67권12호
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• pp.1657-1664
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• 2018

This paper proposes a decision making scheme for choosing the best move at each state of game in order to implement an artificial intelligence othello game player. The proposed decision making scheme predicts the various possible states of the game when the game has progressed from the current state, evaluates the degree of possibility of winning or losing the game at the states, and searches the best move based on the evaluation. In this paper, we generate learning data by decomposing the records of professional players' real game into states, matching and accumulating winning points to the states, and using the Artificial Neural Network that learned them, we evaluated the value of each predicted state and applied the Minimax search to determine the best move. We implemented an artificial intelligence player of the Othello game by applying the proposed scheme and evaluated the performance of the game player through games with three different artificial intelligence players.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.107-112
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• 2022

The automotive industry is motivated to provide more and more amenities to its customers. The industry is taking advantage of artificial intelligence by increasing different sensors and gadgets in vehicles machoism is forward collision warning, at the same time road accidents are also increasing which is another concern to address. So there is an urgent need to provide an A.I based system to avoid such incidents which can be address by using artificial intelligence and global positioning system. Automotive/smart vehicles protection has become a major study of research for customers, government and also automotive industry engineers In this study a two layered novel hypothetical approach is proposed which include in-time vehicle/obstacle detection with auto warning mechanism for collision detection & avoidance and later in a case of an accident manifestation GPS & video camera based alerts system and interrupt generation to nearby ambulance or rescue-services units for in-time driver rescue.

• Earthquakes and Structures
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• 제24권2호
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• pp.111-126
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• 2023

Highly reliable and versatile methods artificial intelligence (AI) have found multiple application in the different fields of science, engineering and health care system. In the present study, we aim to utilize AI method to investigated vibrations in the human leg bone. In this regard, the bone geometry is simplified as a thick cylindrical shell structure. The deep neural network (DNN) is selected for prediction of natural frequency and critical buckling load of the bone cylindrical model. Training of the network is conducted with results of the numerical solution of the governing equations of the bone structure. A suitable optimization algorithm is selected for minimizing the loss function of the DNN. Generalized differential quadrature method (GDQM), and Hamilton's principle are used for solving and obtaining the governing equations of the system. As well as this, in the results section, with the aid of AI some predictions for improving the behaviors of the various sport systems will be given in detail.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.496-498
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• 2023

Brain tumor segmentation problem has challenges in the tumor diversity of location, imbalance, and morphology. Attention mechanisms have recently been used widely to tackle medical segmentation problems efficiently by focusing on essential regions. In contrast, the fusion approaches enhance performance by merging mutual benefits from many models. In this study, we proposed a 3D dual fusion attention network to combine the advantages of fusion approaches and attention mechanisms by residual self-attention and local blocks. Compared to fusion approaches and related works, our proposed method has shown promising results on the BraTS 2018 dataset.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.151-153
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• 2023

Federated learning (FL)-based network intrusion detection techniques have enormous potential for securing the Industrial Internet of Things (IIoT) cybersecurity. The openness and connection of systems in smart industrial facilities can be targeted and manipulated by malicious actors, which emphasizes the significance of cybersecurity. The conventional centralized technique's drawbacks, including excessive latency, a congested network, and privacy leaks, are all addressed by the FL method. In addition, the rich data enables the training of models while combining private data from numerous participants. This research aims to create an FL-based architecture to improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• 디지털융복합연구
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• 제9권2호
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• pp.53-63
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• 2011

소셜네트워크를 통해 각 개인들은 정보를 공유하고, 이러한 공유된 정보와 지식을 바탕으로 집단지성은 형성되고 성장한다. 본 연구는 집단의 능력이라 할 수 있는 집단지성을 측정하고, 집단지성의 형성과정을 관찰하는 것이 목적으로, 5개의 실험집단을 형성하여 기업 경영게임을 실시하게 하였고 집단이 게임을 이용해 수행해야 하는 과업을 주었다. 또한 각 구성원간 소통할 수 있는 네트워크 공간을 만들어 주었으며, 기업경영게임을 통한 과업을 실시하는 동안 네트워크 공간에서 각 구성원간 상호작용과 피드백의 빈도 및 집단의 참여율을 관찰하였다. 연구결과 집단마다 상이한 게임성과를 얻었으며, 5개의 집단의 게임성과의 평균을 기준으로 성과가 높은 집단과 낮은집단으로 나누어 t-test한 결과 상호작용과 피드백의 빈도 및 집단지성참여에 유의미한 차이를 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.11-21
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• 1992

Modern manufacturing process requires machine intelligence to meet the demands for high technology products as well as intelligence-based operating skills to lessen human worker's intervene. To meet this trend there has been wide spread interest in applying artificial neural network(ANN) to the areas of manufacturing process monitoring and control. This paper addresses application problems in such processes as welding, assembly, hydroforming process and inspection of solder joints.

• 한국지능시스템학회논문지
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• 제11권1호
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• pp.65-69
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• 2001

본 논문에서는, 고 중량 측정 장치를 위한 적응 지능형 모니터링 시스템(Adaptive Intelligence Monitoring System ; AIMS)을 제안한다. 지능형 알고리즘으로 퍼지 알고리즘과 FNN을 적용하였으며 고 신뢰도를 가지는 적응 지능형 모니터링 네트웍 시스템의 효용성을 확인한다.