• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.99-106
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• 2014

Generation and analysis methods have been proposed in recent years, such as using a natural language and formal language processing, artificial intelligence algorithms based knowledge model is effective meaning. its semantic based knowledge model has been used effective decision making tree and problem solving about specific context. and it was based on static generation and regression analysis, trend analysis with behavioral model, simulation support for macroeconomic forecasting mode on especially in a variety of complex systems and social network analysis. In this study, in this sense, integrating knowledge-based models, This paper propose a text mining derived from the inter-Topic model Integrated formal methods and Algorithms. First, a method for converting automatically knowledge map is derived from text mining keyword map and integrate it into the semantic knowledge model for this purpose. This paper propose an algorithm to derive a method of projecting a significant topic map from the map and the keyword semantically equivalent model. Integrated semantic-based knowledge model is available.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.417-425
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• 2022

The 'Comprehensive Plan for Nurturing Digital Talents' reported at the Cabinet meeting of the Ministry of Education in August 2022 focuses on qualitative and quantitative expansion of informatics education centered on SW, AI education. With the advent of the era of artificial intelligence, data science education is also drawing attention as a field of informatics education. Data science is originally a field where various studies are fused, and advanced technologies are being used for data analysis, modeling, and machine learning. This study devised a draft of the instructional model of data science education through literature research and analysis of previous studies, and developed a final instructional model through usability test and expert validation.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.1-8
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• 2010

With recent development of computer hardware and 3D graphic technique, a lot of people have concern for something to express as the 3D graphic that look the real environment. Because the request of users have increased, the 3D simulation is developed and popularized in the many field. In this paper, we design and implement the simulation system that humans evacuate a building fires using the 3D graphic techniques. In this paper, we use the A* algorithm to humans have the artificial intelligence at evacuating a building fires, calculate the evacuation speed of each human considering temperature damage and smoke damage. In this paper, we applied the real building to demonstrate the effect of proposed evacuation simulation. Experimental results showed that the evacuation speed is affected by the temperature condition and the smoke density.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.609-612
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• 2021

Government agencies and companies are establishing and promoting digital transformation strategies in various industrial fields, and are leading the era of the 4th industrial revolution through successful technological innovation. In this time of change, we can see many stories of global companies Nike and Starbucks as successful examples of digital transformation. These two companies are showing successful results through digital transformation. Domestic companies are also conducting digital innovation based on mobile, cloud, IoT, artificial intelligence, and AR/VR technologies, and are establishing RPA (Robotic Process Automation) processes for high efficiency and high productivity. In this paper, we introduce the 3D digital twin space construction automation process technique using data from the entire construction cycle of design, construction, and maintenance of the construction industry, and look into the digital transformation strategy of the construction industry in the future.

• Membrane Journal
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• v.33 no.1
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• pp.34-45
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• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.81-83
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• 2022

Understanding research trends is necessary to select research topics and explore related works. Most researchers search representative keywords of interesting domains or technologies to understand research trends. However some conferences in artificial intelligence or data mining fields recently publish hundreds to thousands of papers for each year. It makes difficult for researchers to understand research trend of interesting domains. In our paper, we propose an automatic technology keyphrase extraction method to support researcher to understand research trend for each conference or journal. Keyphrase extraction that extracts important terms or phrases from a text, is a fundamental technology for a natural language processing such as summarization or searching, etc. Previous keyphrase extraction technologies based on pretrained language model extract keyphrases from long texts so performances are degraded in short texts like titles of papers. In this paper, we propose a techonolgy keyphrase extraction model that is robust in short text and considers the importance of the word.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.76-81
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• 2023

To distinguish cancer cells from normal cells, H&E (Hematoxylin & Eosin) staining is required. Pathological staining requires a lot of money and time. Recently, a digital dyeing method has been introduced to reduce such cost and time. In this paper, we propose a novel digital pathology algorithms. The first algorithm is the Pair method. This method learns the dyed phase image and unstained amplitude image taken by FPM (Fourier Ptychographic Microscopy) and converts it into a dyed amplitude image. The second algorithm is the unpair method. This method use the stained and unstained fluorescence microscopic images for modeling. In this study, digital staining was performed using a generative adversarial network (GAN). From the experimental results, we noticed that both the pair and unpair algorithms shows the excellent performance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.883-888
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• 2023

Since data centers are places that provide IT services 24 hours a day, 365 days a year, data center power consumption is expected to increase to approximately 10% by 2030, and the introduction of high-density IT equipment will gradually increase. In order to ensure the stable operation of IT equipment, various types of research are required to conserve energy in cooling and improve energy management. This study proposes the following process for energy saving in data centers. We conducted CFD modeling of the data center, proposed an artificial intelligence-based thermal environment prediction model, compared actual measured data, the predicted model, and the CFD results, and finally evaluated the data center's thermal management performance. It can be seen that the predicted values of RCI, RTI, and PUE are also similar according to the normalization used in the normalization method. Therefore, it is judged that the algorithm proposed in this study can be applied and provided as a thermal environment prediction model applied to data centers.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.465-470
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• 2024

The advancement of AI-based image generation technology has resulted in the creation of various images, emphasizing the need for technology capable of accurately discerning them. The amount of generated image data is limited, and to achieve high performance with a limited dataset, this study proposes a model for discriminating generated images using transfer learning. Applying pre-trained models from the ImageNet dataset directly to the CIFAKE input dataset, we reduce training time cost followed by adding three hidden layers and one output layer to fine-tune the model. The modeling results revealed an improvement in the performance of the model when adjusting the final layer. Using transfer learning and then adjusting layers close to the output layer, small image data-related accuracy issues can be reduced and generated images can be classified.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.31-34
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• 2024

태양광 에너지는 탄소 중립 이행을 위한 주요 방안으로 많은 주목을 받고 있다. 태양광 발전량은 여러 환경적 요인에 따라 크게 달라질 수 있으므로, 정확한 발전량 예측은 전력 네트워크의 안정성과 효율적인 에너지 관리에 근본적으로 중요하다. 대표적인 인공지능 기술인 신경망(Neural Network)은 불안정한 환경 변수와 복잡한 상호작용을 효과적으로 학습할 수 있어 태양광 발전량 예측에서 우수한 성능을 도출하였다. 하지만, 신경망은 모델의 구조나 초매개변수(Hyperparameter)를 최적화하는 것은 복잡하고 시간이 많이 드는 작업이므로, 에너지 분야에서 실제 산업 적용에 한계가 존재한다. 본 논문은 2단계 신경망 최적화를 통한 태양광 발전량 예측 기법을 제안한다. 먼저, 태양광 발전량 데이터 셋을 훈련 집합과 평가 집합으로 분할한다. 훈련 집합에서, 각기 다른 은닉층의 개수로 구성된 여러 신경망 모델을 구성하고, 모델별로 Optuna를 적용하여 최적의 초매개변숫값을 선정한다. 다음으로, 은닉층별 최적화된 신경망 모델을 이용해 훈련과 평가 집합에서는 각각 5겹 교차검증을 적용한 발전량 추정값과 예측값을 출력한다. 마지막으로, 스태킹 앙상블 방식을 채택해 기본 초매개변숫값으로 설정해도 우수한 성능을 도출하는 랜덤 포레스트를 이용하여 추정값을 학습하고, 평가 집합의 예측값을 입력으로 받아 최종 태양광 발전량을 예측한다. 인천 지역으로 실험한 결과, 제안한 방식은 모델링이 간편할 뿐만 아니라 여러 신경망 모델보다 우수한 예측 성능을 도출하였으며, 이를 바탕으로 국내 에너지 산업에 이바지할 수 있을 것으로 기대한다.