• 한국응급구조학회지
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• 제25권3호
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• pp.111-125
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• 2021

Purpose: This study aimed to analyze the impact of rapid cycle deliberate practice (RCDP) simulation education on advanced cardiac life support knowledge, confidence, satisfaction, and performance ability among paramedic students, and provide basic data on the appropriate methods of educational instruction. Methods: The 48 subjects to be instructed were divided into the traditional simulation education group and the RCDP simulation education group. Six participants were randomly assigned to each group and pre-surveyed. They were then exposed to a lecture about advanced cardiac life support related theories for 60 min and post-surveyed through questionnaires with the same learning goals and scenarios. Results: The advanced cardiac life support knowledge (t=-4.813, p=.000) and performance ability (t=-2.903, p=.006) were significantly different between the traditional simulation education and RCDP simulation education groups The results also showed a significant difference in attach monitor (z=6.857, p=.009), analyze EKG rhythm (z=11.111, p=.001), and defibrillation (z=12.632, p=.000), indicating differences in performance capabilities between the two groups. Conclusion: To improve advanced cardiac life support knowledge, performance ability, and confidence in the paramedic students who receive RCDP simulation education, simulation education methods that are appropriate for the subjects being taught, and detailed learning goals and feedback are necessary.

• 한국측량학회지
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• 제40권5호
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• pp.381-391
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• 2022

본 연구는 AI 기법 중에 최근 널리 사용되고 있는 딥러닝 모델들을 비교하여 재난으로 인해 손상된 건물의 신속한 감지에 가장 적합한 모델을 선정하는 데 목적이 있다. 먼저, 신속한 객체감지에 적합한 1단계 기반 검출기 중 주요 딥러닝 모델인 SSD-512, RetinaNet, YOLOv3를 후보 모델로 선정하였다. 이 방법들은 1단계 기반 검출기 방식을 적용한 모델로서 객체 인식 분야에 널리 이용되고 있다. 이 모델들은 객체 인식 처리방식의 구조와 빠른 연산의 장점으로 인해 객체 인식 분야에 널리 사용되고 있으나 재난관리에서의 적용은 초기 단계에 머물러 있다. 본 연구에서는 피해감지에 가장 적합한 모델을 찾기 위해 다음과 같은 과정을 거쳤다. 먼저, 재난에 의한 건물의 피해 정도 감지를 위해 재난에 의해 손상된 건물로 구성된 xBD 데이터셋을 활용하여 초고해상도 위성영상을 훈련시켰다. 다음으로 모델 간의 성능을 비교·평가하기 위하여 모델의 감지 정확도와 이미지 처리속도를 정량적으로 분석하였다. 학습 결과, YOLOv3는 34.39%의 감지 정확도와 초당 46개의 이미지 처리속도를 기록하였다. RetinaNet은 YOLOv3보다 1.67% 높은 36.06%의 감지 정확도를 기록하였으나, 이미지 처리속도는 YOLOv3의 3분의 1에 그쳤다. SSD-512는 두 지표에서 모두 YOLOv3보다 낮은 수치를 보였다. 대규모 재난에 의해 발생한 피해 정보에 대한 신속하고 정밀한 수집은 재난 대응에 필수적이다. 따라서 본 연구를 통해 얻은 결과는 신속한 지리정보 취득이 요구되는 재난관리에 효과적으로 활용될 수 있을 것이라 기대한다.

• 디지털산업정보학회논문지
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• 제5권1호
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• pp.127-139
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• 2009

Recently a rapid development of Information Technology including Internet have brought new way of education such as Distance education, Cyber University, Home Schooling and so on. This change of education have also brought about the change of a tool and medium for education. It is a using multimedia contents in education. Using the multimedia learning contents on line education is considered as one of new way of education and expected to bring learner's better performance. Therefore, it's necessary to research online education contents and its design. In this paper, we discussed how the multimedia contents should be designed to help to reinforce what the learner have learned and researched a critical factor of online contents to effect on learner's better performance.

• Journal of Advanced Marine Engineering and Technology
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• 제26권5호
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• pp.581-588
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• 2002

A fuzzy learning control algorithm is proposed in this paper. In this method, two fuzzy controllers are used as a feedback and a feedforward type. The fuzzy feedback controller can be designed using simple knowledge for the controlled system. On the other hand, the fuzzy feedforward controller has a self-organizing mechanism and therefore, it does not need any knowledge in advance. The effectiveness of the proposed algorithm is demonstrated by experiment on the position and force control problem of a parallelogram type robot manipulator with two degrees of freedom. It is shown that the rapid learning and the robustness can be achieved by adopting the proposed method.

• Journal of Communications and Networks
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• 제13권2호
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• pp.125-131
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• 2011

Rapid strides are being made to bring to reality the technology of wearable sensors for monitoring patients' physiological data.We study the problem of automatically detecting anomalies in themeasured blood glucose levels. The normal daily measurements of the patient are used to train a hidden Markov model (HMM). The structure of the HMM-its states and output symbols-are selected to accurately model the typical transitions in blood glucose levels throughout a 24-hour period. The learning of the HMM is done using historic data of normal measurements. The HMM can then be used to detect anomalies in blood glucose levels being measured, if the inferred likelihood of the observed data is low in the world described by the HMM. Our simulation results show that our technique is accurate in detecting anomalies in glucose levels and is robust (i.e., no false positives) in the presence of reasonable changes in the patient's daily routine.

• Journal of information and communication convergence engineering
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• 제17권4호
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1403-1417
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• 2019

With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of $L_{2,q}$ norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.374-386
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• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.17-24
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• 2023

Given the surge in the elderly population, and increasing in dementia cases, there is a growing interest in digital therapies that facilitate steady remote treatment. However, in the cognitive assessment of digital therapies through clinical trials, the absence of log data as an essential evaluation factor is a significant issue. To address this, we propose a solution of utilizing weighted derived variables based on high-importance variables' accuracy in log data utilization as an indirect cognitive assessment factor for digital therapies. We have validated the effectiveness of this approach using machine learning techniques such as XGBoost, LGBM, and CatBoost. Thus, we suggest the use of log data as a rapid and indirect cognitive evaluation factor for digital therapy users.