• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.287-289
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• 2021

스마트농업 교육은 자기주도형 학습으로 제공되어 공간,장소,시간에 제약을 받지 않으며 서비스 사용자의 현재 상태를 감시하여 서비스 제공에 반영시킬 수 있도록 상황인지 기능을 가진다. 본 논문에서는 스마트농업 교육서비스의 개념과 교육 서비스를 위한 요구사항 그리고 참조구조의 교육 표준 모델을 분석한다. 또한 스마트 농업 교육 서비스의 확산을 위해 필수적으로 요구되는 네트워크 기반에서의 교육 서비스의 모델을 제시하여 스마트 농업 국내외 표준화 및 농업정보의 확산에 기여하리라 판단된다.

• Journal of Information Technology Applications and Management
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• 제26권5호
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• pp.27-39
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• 2019

The Internet of Things (IoT) is a new paradigm that is revolutionizing computing. It is intended that all objects around us will be connected to the network, providing "anytime, anywhere" access to information. This study introduces IoT with Kolb's learning style in order to enhance the learning experience especially for inclusive education for primary and secondary schools where delivery of knowledge is not limited to physical, cognitive disabilities, human diversity with respect to ability, language, culture, gender, age and of other forms of human differences. The article also emphasizes the role of learning style as a discovery process that incorporates the characteristics of problem solving and learning. Kolb's Learning Style was chosen as it is widely used in research and in practical information systems applications. A consistent pattern of finding emerges by using a combination of Kolb's learning style and internet of things where specific individual differences, learning approach differences and IoT application differences are taken as a main research framework. Further several suggestions were made by using this combination to IoT architecture and smart environment of internet of things. Based on these suggestions, future research directions are proposed.

• Smart Structures and Systems
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• 제32권2호
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• pp.111-121
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• 2023

This study proposes an automated assembly performance evaluation method for prefabricated steel structures (PSSs) using machine learning methods. Assembly component images were segmented using a modified version of the receptive field pyramid. By factorizing channel modulation and the receptive field exploration layers of the convolution pyramid, highly accurate segmentation results were obtained. After completing segmentation, the positions of the bolt holes were calculated using various image processing techniques, such as fuzzy-based edge detection, Hough's line detection, and image perspective transformation. By calculating the distance ratio between bolt holes, the assembly performance of the PSS was estimated using the k-nearest neighbors (kNN) algorithm. The effectiveness of the proposed framework was validated using a 3D PSS printing model and a field test. The results indicated that this approach could recognize assembly components with an intersection over union (IoU) of 95% and evaluate assembly performance with an error of less than 5%.

• 한국컴퓨터정보학회논문지
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• 제27권9호
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• pp.49-57
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• 2022

현재 우리 사회는 기후 문제와 세계 인구 증가로 인해 식량 부족 문제가 대두되고 있다. 이를 해결할 방안으로 인공지능(Artificial Intelligent, AI)와 정보통신기술(Information and Communication Technology, ICT)을 접목 시킨 다중 원격 제어 스마트팜을 제안한다. 제안하는 스마트팜은 ICT 기술을 접목시켜 공간과 시간에 제약 없이 원격으로 제어 및 관리하고 작물의 생육환경을 다중 제어한다. 아두이노를 활용하여 스마트폰 애플리케이션(Application, APP)을 통한 다중 제어가 가능한 스마트팜 시스템을 제안하였고, 딥러닝 기술을 적용하여 작물의 생장을 실시간으로 관찰하면서 다양한 데이터 확보 및 진단 기능을 가지는 AI기술을 포함하였다. 스마트팜 내의 각종 센서들을 제어하고 센서들의 데이터 값을 구축한 데이터베이스에 저장하여 사용자가 APP을 통하여 확인할 수 있도록 하였다. 사용자는 APP에서 현재 기상을 참고하여 제어할 수 있도록 하였고 캠을 통해 생육 환경을 실시간으로 확인할 수 있다. 다중 작물을 위한 다중 제어에는 2개 이상의 생육 환경에 대한 각각의 LED, COOLING FAN, WATER PUMP를 적용하여 사용자가 편리하게 제어할 수 있도록 구현하였다. 그리고 딥러닝 기술을 사용하여 TensorFlow 프레임워크를 통해 생육 단계를 진단해주는 APP을 구현하여 사용자가 현재 작물이 어느 단계의 생육 상태인지 손쉽게 진단할 수 있도록 도와주는 애플리케이션을 개발 하고 적용하였다.

• 스마트미디어저널
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• 제13권3호
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• pp.53-61
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• 2024

본 논문은 기존 방법보다 우수한 성능을 달성하는 피부 병변 분할을 위한 새로운 M자 모양 인코더-디코더 아키텍처를 제안한다. 제안된 아키텍처는 왼쪽과 오른쪽 다리를 활용하여 다중 스케일 특징 추출을 가능하게 하고, 스킵 연결 내에서 어텐션 메커니즘을 통합하여 피부 병변 분할 성능을 더욱 향상시킨다. 입력 영상은 네 가지 다른 패치로 분할되어 입력되며 인코더-디코더 프레임워크 내에서 피부 병변 분할 성능의 향상된 처리를 가능하게 한다. 제안하는 방법에서 어텐션 메커니즘을 통해 입력 영상의 특징에 더 많은 초점을 맞추어 더욱 정교한 영상 분할 결과를 도출하는 것이다. 실험 결과는 제안된 방법의 효과를 강조하며, 기존 방법과 비교하여 우수한 정확도, 정밀도 및 Jaccard 지수를 보여준다.

• International journal of advanced smart convergence
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• 제8권1호
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• pp.24-34
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• 2019

In this research, a practical deep learning framework to differentiate the lesions and nodules in breast acquired with ultrasound imaging has been proposed. 7408 ultrasound breast images of 5151 patient cases were collected. All cases were biopsy proven and lesions were semi-automatically segmented. To compensate for the shift caused in the segmentation, the boundaries of each lesion were drawn using Fully Convolutional Networks(FCN) segmentation method based on the radiologist's specified point. The data set consists of 4254 benign and 3154 malignant lesions. In 7408 ultrasound breast images, the number of training images is 6579, and the number of test images is 829. The margin between the boundary of each lesion and the boundary of the image itself varied for training image augmentation. The training images were augmented by varying the margin between the boundary of each lesion and the boundary of the image itself. The images were processed through histogram equalization, image cropping, and margin augmentation. The networks trained on the data with augmentation and the data without augmentation all had AUC over 0.95. The network exhibited about 90% accuracy, 0.86 sensitivity and 0.95 specificity. Although the proposed framework still requires to point to the location of the target ROI with the help of radiologists, the result of the suggested framework showed promising results. It supports human radiologist to give successful performance and helps to create a fluent diagnostic workflow that meets the fundamental purpose of CADx.

• 스마트미디어저널
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• 제11권8호
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• pp.84-92
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• 2022

Precision livestock monitoring promises greater management efficiency for farmers and higher welfare standards for animals. Recent studies on video-based animal activity recognition and tracking have shown promising solutions for understanding animal behavior. To achieve that, surveillance cameras are installed diagonally above the barn in a typical cattle farm setup to monitor animals constantly. Under these circumstances, tracking individuals requires addressing challenges such as occlusion and visual appearance, which are the main reasons for track breakage and increased misidentification of animals. This paper presents a framework for multi-cattle tracking in closed barns with appearance and motion models. To overcome the above challenges, we modify the DeepSORT algorithm to achieve higher tracking accuracy by three contributions. First, we reduce the weight of appearance information. Second, we use an Ensemble Kalman Filter to predict the random motion information of cattle. Third, we propose a supplementary matching algorithm that compares the absolute cattle position in the barn to reassign lost tracks. The main idea of the matching algorithm assumes that the number of cattle is fixed in the barn, so the edge of the barn is where new trajectories are most likely to emerge. Experimental results are performed on our dataset collected on two cattle farms. Our algorithm achieves 70.37%, 77.39%, and 81.74% performance on HOTA, AssA, and IDF1, representing an improvement of 1.53%, 4.17%, and 0.96%, respectively, compared to the original method.

• 한국IT서비스학회지
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• 제11권4호
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• pp.309-333
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• 2012

Recently, emergence of smart-phones and Social Networking Service(SNS) would offer the market environment changes and the opportunities for new business. For the case analysis comprehensive survey were implemented. And those data were analyzed along the research framework. The late-comer firms offered differential services, maintained creative and opened corporate culture, shoed learning capabilities which means absorption and organization of external knowledge, innovative efforts to control the insurgents than early-mover firms. When we analyze these phenomena along the developmental stages of late-comer, we can perceive that the stage of late-comers firms were moving from the "tracing the path" stage to "jumping the path" stage which means the creating capabilities were more or less enhanced and the firms become more stable in terms of business operation. In business model, early-mover firms showed clear definition for each business element, especially the revenue structure, while late-mover firms seemed unstable or unclear revenue structure.

• 한국컴퓨터정보학회논문지
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• 제28권12호
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• pp.67-77
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• 2023

본 연구는 스마트팜 환경에서 진행된 혁신적인 연구로, 딥러닝을 기반으로 한 질병 및 해충 탐지 모델을 개발하고, 이를 지능형 사물인터넷(IoT) 플랫폼에 적용하여 디지털 농업 환경 구현의 새로운 가능성을 탐색하였다. 연구의 핵심은 Pseudo-Labeling, RegNet, EfficientNet 등 최신 ImageNet 모델과 전처리 방식을 통합하여, 복잡한 농업 환경에서 다양한 질병과 해충을 높은 정확도로 탐지하는 것이었다. 이를 위해 앙상블 학습 기법을 적용하여 모델의 정확도와 안정성을 극대화했으며, 평균 정밀도(mAP), 정밀도, 재현율, 정확도, 박스 손실 등의 다양한 성능 지표를 통해 모델을 평가하였다. 또한, SHAP 프레임워크를 활용하여 모델의 예측 기준에 대한 깊은 이해를 도모하였고, 이를 통해 모델의 결정 과정을 보다 투명하게 만들었다. 이러한 분석은 모델이 어떻게 다양한 변수들을 고려하여 질병 및 해충을 탐지하는지에 대한 중요한 통찰력을 제공하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2529-2549
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• 2023

For effectively lowering down the risk of cyber threating, the zero-trust architecture (ZTA) has been gradually deployed to the fields of smart city, Internet of Things, and cloud computing. The main concept of ZTA is to maintain a distrustful attitude towards all devices, identities, and communication requests, which only offering the minimum access and validity. Unfortunately, adopting the most secure and complex multifactor authentication has brought enterprise and employee a troublesome and unfriendly burden. Thus, authors aim to incorporate machine learning technology to build an employee behavior analysis ZTA. The new framework is characterized by the ability of adjusting the difficulty of identity verification through the user behavioral patterns and the risk degree of the resource. In particular, three key factors, including one-time password, face feature, and authorization code, have been applied to design the adaptive multifactor continuous authentication system. Simulations have demonstrated that the new work can eliminate the necessity of maintaining a heavy authentication and ensure an employee-friendly experience.