• Structural Engineering and Mechanics
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• v.16 no.6
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• pp.643-654
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• 2003

Two real-time modeling prediction (RMP) schemes are presented in this paper for analyzing the behavior of deep excavations during construction. The first RMP scheme is developed from the traditional AR(p) model. The second is based on the simplified Elman-style recurrent neural networks. An on-line learning algorithm is introduced to describe the dynamic behavior of deep excavations. As a case study, in-situ measurements of an excavation were recorded and the measured data were used to verify the reliability of the two schemes. They proved to be both effective and convenient for predicting the behavior of deep excavations during construction. It is shown through the case study that the RMP scheme based on the neural network is more accurate than that based on the traditional AR(p) model.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.579-583
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• 1988

As the power facilities grow up, the optimal operation and the best maintenance of power plant can not be overestimated too much, which can enhance the plant availability and reliability much further. In this respect, fault diagnosis methodologies of dynamic system which is time-varing and strongly nonlinear have been studied. On of them is to use algorithm which is based on time-invariant, linear system, but this is not so nice a method for applying to power Plant. Therefore, the study on other techniques using Artificial Intelligence (AI) is under way. In this paper, the existing ways of fault detection are surveyed and their problems are also discussed.

• Korean Journal of Materials Research
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• v.8 no.11
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• pp.982-986
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• 1998

baking 전후의 금선의 접합강도 변화를 연구하였다. 금선을 이용하여 Si 칩의 AI 패드와 은도금된 리드프레임 사이를 thermosonic 방법으로 본딩하였다. 본딩된 금선을 $175^{\circ}C$에서 시간을 변화시키면서 baking 처리하였다. 접합강도는 와이어 풀 테스트, 볼 전단 테스트, stud 풀 테스트로 평가하였다. 와이어 풀 접합강도는 baking 처리를 거쳐도 크게 변화하지 않았지만 파괴 유형이 baking 전에는 볼목 파괴에서 baking 후 스티치 파괴로 바뀌었다. 본딩과 baking 중 금선의 결정립이 크게 성장하였는데 이런 결정립 크기 변화와 금선 접합 부위의 기하학적인 모양에 따라 파괴 유형이 바뀌었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1478-1481
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• 2019

As core technologies(IoT, 5G, Cloud, Bigdata, AI etc) leading the Fourth Industrial Revolution promote smart convergence across the national socio-economic infrastructure, the threat of new forms of cyber attacks is increasing and the possibility of massive damage is also increasing. Reflecting this trend, cyber security is expanding from simple information protection to CPS(Cyber Physical System) protection that combines safety and security that implements hyper-connectivity and ultra-reliability. This study introduces the recent evolution of cyber attacks and looks at the next generation cyber security technologies based on the conceptual changes of cyber security technologies such as SOAR(Security Orchestration, Automation and Response) and Zero Trust.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.1
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• pp.1-8
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• 2021

Detecting Out-of-Distribution (OOD) data is fundamentally required when Deep Neural Network (DNN) is applied to real-world AI such as autonomous driving. However, modern DNNs are quite vulnerable to the over-confidence problem even if the test data are far away from the trained data distribution. To solve the problem, this paper proposes a novel Adversarial-Mixup training method to let the DNN model be more robust by detecting OOD data effectively. Experimental results show that the proposed Adversarial-Mixup method improves the overall performance of OOD detection by 78% comparing with the State-of-the-Art methods. Furthermore, we show that the proposed method can alleviate the over-confidence problem by reducing the confidence score of OOD data than the previous methods, resulting in more reliable and robust DNNs.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.142-151
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• 2022

In this paper, a model combined with explanatory artificial intelligence (xAI) models was presented to secure the reliability of machine learning-based sentiment analysis and prediction. The applicability of the proposed model was tested and described using the IMDB dataset. This approach has an advantage in that it can explain how the data affects the prediction results of the model from various perspectives. In various applications of sentiment analysis such as recommendation system, emotion analysis through facial expression recognition, and opinion analysis, it is possible to gain trust from users of the system by presenting more specific and evidence-based analysis results to users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.663-677
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• 2023

As the paradigm of future vehicles changes, the interest in automotive semiconductor, which plays a key role in realizing this, is increasing. Automotive semiconductors are the technology with very high entry barriers that require a lot of effort and time because it must secure technology readiness level and also consider safety and reliability. In this technology field, it is very important to develop new businesses and create opportunities through technology trend analysis. However, systematic analysis and application of automotive semiconductor technology trends are currently lacking. In this paper, U.S. registered patent documents related to automotive semiconductor were collected and investigated based on the patent's IPC. The main technology of automotive semiconductor was analyzed through topic modeling, and the technology path such as emerging technology was investigated through cosine similarity. We identified that those emerging technologies such as driving control for vehicle and AI service appeared. We observed that as time passed, both convergence and independence of automotive semiconductor technology proceeded simultaneously.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.445-445
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• 2023

하천수 수요는 생활, 공업, 농업 등 인위적 사용 그리고 하천의 환경개선, 건천화 방지 및 물 재이용 등과 관련된 하천유지유량으로 구분된다. 현행 수자원 관리는 갈수 시에도 안정적인 취수가 가능하도록 운영하고 있어, 하천수 취수(사용)의 신규 또는 변경 허가를 위한 허가기준유량의 개념 및 관리는 자연, 사회적 하천수 수요 관리에 매우 중요한 인자이다. 본 연구에서는 하천수의공급 능력을 평가하기 위하여, 댐의 이수안전도를 나타내는 지표를 이용하여 허가기준유량을 평가하였다. 신뢰도(reliability), 회복도(resilience) 및 취약도(vulnerability)를 기준으로 하천유지유량고시 지점과 유량 조사 지점이었고 분석 결과, 부족 발생 시에 이를 회복하는 능력에서 지점별 큰 차이가 확인되었고, 이로부터 하천수 관리 현황과 개선점을 제시하였다.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.409-414
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• 2021

The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2086-2097
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• 2021

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.