• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.35-42
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• 2022

The article identifies the problem of monitoring the quality of higher education in three main areas, which are comparative pedagogical systems of education. The first direction is determined by dissertation works, the second - monographs and textbooks, and the third reveals scientific periodicals. According to its internal structure, monitoring the quality of education combines important management components identified in the article (analysis, evaluation and forecasting of processes in education; a set of methods for tracking processes in education; collecting and processing information to prepare recommendations for research processes and make necessary adjustments). Depending on the objectives, three areas of monitoring are identified: informational (involves the accumulation, structuring and dissemination of information), basic (aimed at identifying new problems and threats before they are realized at the management level), problematic (clarification of patterns, processes, hazards, those problems that are known and significant from the point of view of management). According to its internal structure, monitoring the quality of education combines the following important management components: analysis, evaluation and forecasting of processes in education; a set of techniques for tracking processes in education; collection and processing of information in order to prepare recommendations for the development of the studied processes and make the necessary adjustments. One of the priorities of the higher education modernization program during the COVID-19 pandemic is distance learning, which is possible due to the existence of information and educational technologies and communication systems, especially for effective education and its monitoring in higher education. The conditions under which the effectiveness of pedagogical support of monitoring activities in the process of distance learning is achieved are highlighted. According to the results of the survey, the problems faced by higher education seekers are revealed. A survey of students was conducted, which had a certain level of subjectivity in personal assessments, but the sample was quite representative.

• 한국환경복원기술학회지
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• 제25권6호
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• pp.25-33
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• 2022

In this study, a monitoring method using high-resolution images acquired by unmanned aerial vehicles and deep learning algorithms was proposed for the management of the Sinduri coastal sand dunes. Class classification was done using U-net, a semantic division method. The classification target classified 3 types of sand dune vegetation into 4 classes, and the model was trained and tested with a total of 320 training images and 48 test images. Ignored label was applied to improve the performance of the model, and then evaluated by applying two loss functions, CE Loss and BCE Loss. As a result of the evaluation, when CE Loss was applied, the value of mIoU for each class was the highest, but it can be judged that the performance of BCE Loss is better considering the time efficiency consumed in learning. It is meaningful as a pilot application of unmanned aerial vehicles and deep learning as a method to monitor and manage sand dune vegetation. The possibility of using the deep learning image analysis technology to monitor sand dune vegetation has been confirmed, and it is expected that the proposed method can be used not only in sand dune vegetation but also in various fields such as forests and grasslands.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• International journal of thyroidology
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• 제11권2호
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• pp.130-136
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• 2018

Background and Objectives: Intraoperative neural monitoring (IONM) of recurrent laryngeal nerve (RLN) in thyroid surgery has been employed worldwide to identify and preserve the nerve as an adjunct to visual identification. The aims of this study was to evaluate the efficacy of IONM and difficulties in the learning curve. Materials and Methods: We studied 63 patients who underwent thyroidectomy with IONM during last 2 years. The standard IONM procedure was performed using NIM 3.0 or C2 Nerve Monitoring System. Patients were divided into two chronological groups based on the success rate of IONM (33 cases in the early period and 30 cases in the late period), and the outcomes were compared between the two groups. Results: Of 63 patients, 32 underwent total thyroidectomy and 31 thyroid lobectomy. Failure of IONM occurred in 9 cases: 8 cases in the early period and 1 case in the late period. Loss of signal occurred in 8 nerves of 82 nerves at risk. The positive predictive value increased from 16.7% in the early period to 50% in the late period. The mean amplitude of the late period was higher than that of the early period (p<0.001). Conclusion: IONM in thyroid surgery is effective to preserve the RLN and to predict postoperative nerve function. However, failure of IONM and high false positive rate can occur in the learning curve, and the learning curve was about 30 cases based on the results of this study.

• 한국컴퓨터정보학회논문지
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• 제27권2호
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• pp.25-32
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• 2022

본 논문에서는 활동 인식장치를 이용한 딥러닝 기반의 반려동물 모니터링 시스템을 제안한다.이 시스템은 반려동물의 활동 인식장치와 반려인의 스마트 기기, 서버로 구성된다. 아두이노 기반 활동 인식 장치로부터 가속도와 자이로 데이터를 수집하고, 이로부터 반려동물의 걸음 수를 연산하였다. 수집된 데이터는 전처리 과정을 거쳐 CNN과 LSTM을 하이브리드한 딥러닝 모델을 통해 5가지 형태(앉기, 서기, 눕기, 걷기, 뛰기)로 활동을 인식함으로써 활동량을 측정한다. 마지막으로, 반려인의 스마트 기기에 일일 및 주간 브리핑 차트 등 활동 변화에 대한 모니터링을 제공한다. 성능 평가 결과, 반려동물의 구체화된 활동 인식 및 활동량 측정이 가능함을 확인하였다. 향후 데이터 축적을 통해 반려동물의 이상행동 탐지 및 헬스 케어 서비스의 확장을 기대할 수 있다.

• 한국가시화정보학회지
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• 제19권2호
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• pp.41-47
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• 2021

River and stream are the important water supply source in our lives. Eutrophication causes excessive green algae growth including microcystis, which makes harmful to ecosystem and human health. Therefore, the water purification process to remove green algae is essential. In Korea, green algae alarm system exists depending on the concentration of green algae cells in river or stream. To maintain the growth amount under control, green algae monitoring system is being used. However, the unmanned, small and automatic monitoring system would be preferable. In this study, we developed the 3D printed device to measure the concentration of green algae cell using microfluidic droplet generator and deep learning. Deep learning network was trained by using transfer learning through pre-trained deep learning network. This newly developed microfluidic cell counter has sufficient accuracy to be possibly applicable to green algae alarm system.

• 한국항행학회논문지
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• 제24권6호
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• pp.620-626
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• 2020

본 연구는 IOT 장비인 라즈베리파이를 마스터(master)로 이용하고 인버터를 슬레이브(slave)로 하여 모드버스 TCP 통신을 기반한 태양광 발전 모니터링 시스템을 제안하였다. 본 모델은 라즈베리파이에 다양한 센서를 추가하여 태양광 발전소의 모니터링에 필요한 정보를 추가하였으며, 실시간 발전량 예측을 통해 발전량 예측과 모니터링 정보를 스마트 폰으로 송신하였다. 또한, 서버에 태양광 발전소에서 지속해서 생성되는 정보를 빅데이터로 구축하였으며, 발전량 예측을 위한 딥러닝 모델을 학습하여 갱신하였다. 연구 결과로서 인버터에서 라즈베리파이로 모드버스 TCP 기반으로 안정적인 통신이 가능하였고, 라즈베리파이에서 학습된 딥러닝 모델로 실시간 예측이 가능하였다. 서버는 빅데이터로 다양한 딥러닝 모델 학습이 가능하였으며, LSTM이 학습 오차 0.0069, 테스트 오차 0.0075, RMSE 0.0866 등으로 가장 좋은 오차를 보임을 확인하였다. 본 모델은 다양한 제조사의 인버터에 대해서 보다 간단하고 편리하며 발전량을 예측할 수 있는 실시간 모니터링 시스템 구현이 가능함을 제시하였다.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 인터넷정보학회논문지
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• 제23권3호
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• pp.97-110
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• 2022

연합학습은 학습데이터의 전송없이 모델의 학습을 수행할 수 있는 학습방법이다. IoT 혹은 헬스케어 분야는 사용자의 개인정보를 다루는 만큼 정보유출에 민감하여 시스템 디자인에 많은 주의를 기울여야 하지만 연합학습을 사용하는 경우 데이터가 수집되는 디바이스에서 데이터가 이동하지 않기 때문에 개인정보 유출에 자유로운 학습방법으로 각광받고 있다. 이에 따라 많은 연합학습 구현체가 개발되었으나 연합학습을 사용하는 시스템의 개발과 운영을 위한 시스템 설계에 관한 구체적인 연구가 부족하다. 본 연구에서는 연합학습을 실제 프로젝트에 적용하여 IoT 디바이스에 배포하고자 할 때 연합학습의 수명주기, 코드 버전 관리, model serving, 디바이스 모니터링에 대한 대책이 필요함을 보이고 이러한 점을 보완해주는 개발환경에 대한 설계를 제안하고자 한다. 본 논문에서 제안하는 시스템은 중단 없는 model-serving을 고려하였고 소스코드 및 모델 버전 관리와 디바이스 상태 모니터링, 서버-클라이언트 학습 스케쥴 관리기능을 포함한다.