• 대기
• /
• 제32권4호
• /
• pp.353-365
• /
• 2022

This paper evaluates precipitation forecast skill of Global/Regional Integrated Model system (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type (C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequencies are 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantly overestimates precipitation occurrence (0.1 mm d^-1) for all types of winter precipitation. Objective measured skill scores of GRIMs are comparably high for L type and O type. Except for precipitation occurrence, the model shows high predictability for L type precipitation with the most unbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuring rare events due to its high dependency on the sample size, as in the case of Critical Success Index as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivity on the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplement the limit of ETS. The evaluation via SEDS shows that the prediction skill score for L type is the highest in the range of 5.0, 10.0 mm d^-1 and the score for O type is the highest in the range of 1.0, 20.0 mm d^-1. C type has the lowest scores in overall range. The difference in precipitation forecast skill by precipitation type can be explained by the spatial distribution and intensity of precipitation in each representative case.

• PNF and Movement
• /
• 제22권1호
• /
• pp.43-54
• /
• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• 한국안전학회지
• /
• 제27권4호
• /
• pp.115-120
• /
• 2012

In order to establish preventive maintenance standards through analysis & reliability prediction of about 60,000pcs of 20kindsof relays and contractors used for Seoul subway trains, several life prediction methodologies were applied. Firstly, Occurrence, Severity, Detection were defined and predicted by applying operation characteristic of EMU to the number of actions of relays & contactors which the manufacturers generally offer as the life cycle data. Secondly, failure distribution and average life of parts were analyzed through interpretation of field data based on a lot of experience which had built up in the field for a long time. Finally, using the 217PLUS standard as a reliability prediction program, comparative analysis of use reliability and inherent reliability was done through reliability prediction at the part level and system level.

• 한국콘크리트학회:학술대회논문집
• /
• 한국콘크리트학회 2006년도 추계 학술발표회 논문집
• /
• pp.957-960
• /
• 2006

In order to predict the accumulated damages by cyclic freeze-thaw, a regression analysis by the Response Surface Method (RSM) is used. RSM has merits when the other probabilistic simulation techniques can not guarantee the convergence of probability of occurrence or when the others can not differentiate the derivative terms of limit state functions, which are composed of random design variables in the model of complex system or the system having higher reliability. For composing limit state function, the important parameters for cyclic freeze-thaw-deterioration of concrete structures, such as water to cement ratio, entrained air pores, and the number of cycles of freezing and thawing, are used as input parameters of RSM. The predicted results of relative dynamic modulus and residual strains after 300 cycles of freeze-thaw for specimens show very good agreements with the experimental results. The RSM result can be used to predict the probability of occurrence for designer specified critical values. Therefore, it is possible to evaluate the life cycle management of concrete structures considering the accumulated damages by the cyclic freeze-thaw by the use of proposed prediction method.

• 농업과학연구
• /
• 제41권1호
• /
• pp.23-27
• /
• 2014

Potato late blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, economic threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of potato late blight.

• 농업과학연구
• /
• 제41권1호
• /
• pp.29-34
• /
• 2014

Phytophthora blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, control threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of Phytophthora blight on hot pepper.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2022년도 가을 학술논문 발표대회
• /
• pp.223-224
• /
• 2022

The occurrence of defect, one of the major risk elements, gives rise to construction delays and additional costs. Although construction companies generally prefer to use a method of identifying and classifying the causes of defects, a system for predicting the rise of defects becomes important matter to reduce this harmful issue. However, the currently used methods are kinds of reactive systems that are focused on the defects which occurred already, and there are few studies on the occurrence of defects with prediction systems. This paper is about preliminary study on the development of judgemental algorithm that informs us whether additional works related to defect issue are needed or not. Among machine learning techniques, deep neural network was utilized as prediction model which is a major component of algorithm. It is the most suitable model to be applied to the algorithm when there are 8 hidden layers and the average number of nodes in each hidden layer is 70. Ultimately, the algorithm can identify and defects that may arise in later and contribute to minimize defect frequency.

• 한국항공운항학회:학술대회논문집
• /
• 한국항공운항학회 2005년도 추계학술발표대회 논문집
• /
• pp.7-10
• /
• 2005

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 1999년도 학술발표회 논문집
• /
• pp.141-146
• /
• 1999

• 한국항공운항학회:학술대회논문집
• /
• 한국항공운항학회 2004년도 추계학술발표대회 논문집
• /
• pp.62-65
• /
• 2004