• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• The Korean Journal of Emergency Medical Services
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• v.15 no.3
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• pp.5-17
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• 2011

purpose : The purpose of this study was to analyze the effect of basic life support performance by video simulation training. Methods : The subjects in this study consisted of 24 persons of experimental group and 24 persons of control group in freshmen and sophomore EMT students. The data were collected from September 1 to 30, 2010. Results : 1. Hypothesis one: experimental group is meaningful higher than control group at the operation point (p<0.05) of adult/infant's basic life support for one person. In subcategory that identifying breath, artificial respiration, pulse rate, 30 compressions, Ability to perform all the processes, the experimental group showed statistically higher score (p<0.05) than control group, and sequence from checking reaction to keeping airway management isn't statistically significant difference between experimental group and control. 2. Hypothesis two: In the hypothesis, the experimental group's point of adult basic life support by two persons and use of Automated External Defibrillator is good in experimental group than control group's point(p<0.05). As the result of researching 11 contents of check list about adult basic life support by two persons and Automated External Defibrillator(AED), by dividing into 7 subcategories, every subcategory shows that the experimental group is significant higher than control group(p<0.05). 3. Hypothesis three: In the hypothesis, the experimental group gets higher point of infant basic life support by one or two persons than the control group (p<0.05). As the results of researching 15 contents of check list about infant basic life support by one or two persons, by dividing into 8 subcategories, the experimental group is statistically meaningful higher (p<0.05) than the control group in process of keeping airway, indentifying breathing, identifying pulse, 30 compressions, Ability to perform all the processes. There isn't statistically significant difference between the groups in process of checking reaction, reporting 119, and artificial respiration by giving 2 breaths(p<0.05). Conclusion : As summarizing the results, the group, receiving using video, gets higher points of knowledge of basic life support and operating skill than the general educated group. It is found that the Video simulation training could be effective, because of these positive effects to improve clinical working performance of students, who participate in the department of Emergency Medical Technology.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.71-80
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• 2020

As societies become hyperconnected, we need more cyber security experts. To this end, in this paper, based on the analysis results of the real world cyber attacks and the MITRE ATT&CK framework, we developed CyTEA that can model cyber threats and generate simulated cyber threats in a cyber security training system. In order to confirm whether the simulated cyber threat has the effectiveness of the actual cyber threat level, the simulation level was examined based on procedural, environmental, and consequential similarities. in addition, it was confirmed that the actual defense training using cyber simulation threats is the same as the expected defense training when using real cyber threats in the cyber security training system.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.145-152
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• 2020

MRC (Machine reading comprehension) is the AI NLP task that predict the answer for user's query by understanding of the relevant document and which can be used in automated consult services such as chatbots. Recently, the BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model, which shows high performance in various fields of natural language processing, have two phases. First phase is Pre-training the big data of each domain. And second phase is fine-tuning the model for solving each NLP tasks as a prediction. In this paper, we have made the Patent MRC dataset and shown that how to build the patent consultation training data for MRC task. And we propose the method to improve the performance of the MRC task using the Pre-trained Patent-BERT model by the patent consultation corpus and the language processing algorithm suitable for the machine learning of the patent counseling data. As a result of experiment, we show that the performance of the method proposed in this paper is improved to answer the patent counseling query.

• Quality Improvement in Health Care
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• v.1 no.1
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• pp.124-131
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• 1994

Polysomnography is an essential methodology for diagnosing and following up sleep disorders and doing researches on human sleep. Sleep medicine, mainly with the utilization of polysomnographic techniques, has developed itself as one of the promising fields in the 21st century medicine. Korea is not an exception in importing and developing sleep medicine into the conventional medicine. However, it still remains to be clarified what polysomnography is for and how it should be done, considering the relatively recent introduction of sleep medicine into Korea. The author, being a board-certified sleep medicine specailist, having experienced spreading out sleep medicine within Korea for the past four years, and having recently set up a major sleep study facility in Korea at Seoul National University Hospital, attempts in this introductory critical article to review the essential issues related to quality assurance in polysomnographic study of human sleep. Also, unconditional introduction of "automated" sleep scoring system, which has been found to have significantly reduced reliability in various studies including the author's own, is critically reviewed. The author suggests that quality assurance and training program should be initiated and established by a responsible sleep medicine-related organization such as the Korean Association of Sleep Medicine and Psychophysiology.

• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.381-389
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• 1999

One of the objectives of any task design is to provide a safe and helpful workplace for the employees. The safety and health module may include means for confronting the design with safety and health regulations and standards as well as tools for obstacles and collisions detection (such as error models and simulators). Virtual Reality is a leading edge technology which has only very recently become available on platforms and at prices accessible to the majority of simulation engineers. The design of an automated manufacturing system is a complicated, multidisciplinary task that requires involvement of several specialists. In this paper, a design procedure that facilitates the safety and ergonomic considerations of an automated manufacturing system are described. The procedure consists of the following major steps: Data collection and analysis of the data, creation of a three-dimensional simulation model of the work environment, simulation for safety analysis and risk assessment, development of safety solutions, selection of the preferred solutions, implementation of the selected solutions, reporting, and training When improving the safety of an existing system the three-dimensional simulation model helps the designer to perceive the work from operators point of view objectively and safely without the exposure to hazards of the actual system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.421-441
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• 2021

Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.335-345
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• 2018

Objectives: The retinal nerve fiber layer (RNFL) is a site of glaucomatous optic neuropathy whose early changes need to be detected because glaucoma is one of the most common causes of blindness. This paper proposes an automated RNFL detection method based on the texture feature by forming a co-occurrence matrix and a backpropagation neural network as the classifier. Methods: We propose two texture features, namely, correlation and autocorrelation based on a co-occurrence matrix. Those features are selected by using a correlation feature selection method. Then the backpropagation neural network is applied as the classifier to implement RNFL detection in a retinal fundus image. Results: We used 40 retinal fundus images as testing data and 160 sub-images (80 showing a normal RNFL and 80 showing RNFL loss) as training data to evaluate the performance of our proposed method. Overall, this work achieved an accuracy of 94.52%. Conclusions: Our results demonstrated that the proposed method achieved a high accuracy, which indicates good performance.