• Information and Communications Magazine
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• v.32 no.4
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• pp.57-62
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• 2015

사물인터넷의 발전과 함께 홈 IoT(사물인터넷 기반 스마트 홈)로 주거 문화가 바뀌고 있다. 과거 고급형 대단위 아파트 중심의 홈 오토메이션 시스템이 스마트폰으로 촉발된 모바일 혁명을 바탕으로 쉽고 편하게 사용할 수 있는 생활 밀착형 스마트 홈으로 전환되고 있으며, 제품 판매 중심이었던 수익모델도 다양한 서비스를 기반으로 하는 플랫폼 사업으로 변화하고 있다. 본고에서는 최근 발전을 거듭하고 있는 홈 IoT 플랫폼과 함께 적외선 제어 방식을 추가한 디지엔스의 홈 IoT 플랫폼을 소개한다.

• Tunnel and Underground Space
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• v.29 no.4
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• pp.230-242
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• 2019

While various automatic rock fracture survey methods have been researched, the evaluation of the accuracy of these methods raises issues due to the absence of a metric which fully expresses the similarity between automatic and manual fracture maps. Therefore, this paper proposes a geometry similarity metric which is especially designed to determine the overall similarity of fracture maps and to evaluate the accuracy of rock fracture survey methods by a single number. The proposed metric, Scanline Intersection Similarity (SIS), is derived by conducting a large number of scanline surveys upon two fracture maps using Python code. By comparing the frequency of intersections over a large number of scanlines, SIS is able to express the overall similarity between two fracture maps. The proposed metric was compared with Intersection Over Union (IoU) which is a widely used evaluation metric in computer vision. Results showed that IoU is inappropriate for evaluating the geometry similarity of fracture maps because it is overly sensitive to minor geometry differences of thin elongated objects. The proposed metric, on the other hand, reflected macro-geometry differences rather than micro-geometry differences, showing good agreement with human perception. The metric was further applied to evaluate the accuracy of a deep learning-based automatic fracture surveying method which resulted as 0.674 (SIS). However, the proposed metric is currently limited to 2D fracture maps and requires comparison with rock joint parameters such as RQD.

• Information and Communications Magazine
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• v.30 no.8
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• pp.11-19
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• 2013

본고에서는 M2M/IoT 통신의 실현을 위한 무선 통신망 기술을 알아보고 IoT 통신을 위한 요구사항과 이를 해결하기 위한 연구 동향을 살펴본다. 특히, 많은 수의 IoT 디바이스가 싱크 노드를 이용하여 IP 망에 접속하는 Wireless Sensor network(WSN)에서의 문제와, LTE-A와 같은 cellular 망을 이용하여 접속하는 IoT 서비스로 나누어 논의한다. WSN관점에서는 에너지에 대한 제약이 심한 환경을 고려하여 발생할 수 문제점들을 분류하고 이에 대한 다양한 해결책을 제시하며, Cellular 망에서는 현재의 LTE-A 망에 많은 수의 IoT 디바이스가 연결될 경우 발생할 수 있는 문제점들을 논하고 기존의 통신에 영향을 최소화 하며 IoT 서비스를 공존할 수 있는 연구 동향을 논한다.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

• Journal of IKEEE
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• v.27 no.4
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• pp.456-463
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• 2023

In this paper, we introduce the SSResUNet network model, which integrates the SPADE structure with the U-Net network model for accurate land cover classification using high-resolution satellite imagery without requiring user intervention. The proposed network possesses the advantage of preserving the spatial characteristics inherent in satellite imagery, rendering it a robust classification model even in intricate environments. Experimental results, obtained through training on KOMPSAT-3A satellite images, exhibit superior performance compared to conventional U-Net and U-Net++ models, showcasing an average Intersection over Union (IoU) of 76.10 and a Dice coefficient of 86.22.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.580-591
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• 2019

This paper focuses on to improving object detection performance using the camera and LiDAR on autonomous vehicle platforms by fusing detected objects from individual sensors through a late fusion approach. In the case of object detection using camera sensor, YOLOv3 model was employed as a one-stage detection process. Furthermore, the distance estimation of the detected objects is based on the formulations of Perspective matrix. On the other hand, the object detection using LiDAR is based on K-means clustering method. The camera and LiDAR calibration was carried out by PnP-Ransac in order to calculate the rotation and translation matrix between two sensors. For Sensor fusion, intersection over union(IoU) on the image plane with respective to the distance and angle on world coordinate were estimated. Additionally, all the three attributes i.e; IoU, distance and angle were fused using logistic regression. The performance evaluation in the sensor fusion scenario has shown an effective 5% improvement in object detection performance compared to the usage of single sensor.

• Information and Communications Magazine
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• v.32 no.4
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• pp.36-43
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• 2015

최근 정보가전 및 네트워크 기술의 발전으로 인하여 IoT 기술에 대한 관심이 증가되고 있다. 기존의 단일 네트워크 상에서 제한적으로 구현되던 홈 네트워크는 네트워크 기술의 발전에 힘입어 언제, 어디서든 사용자가 원하는 기기에 접근하고 제어할 수 있도록 발전하고 있다. 주요 정보가전 제조사 및 서비스 업체에서는 IoT 기술을 적용한 제품과 서비스들을 경쟁적으로 출시하고 있으며, 관련 표준 기구들도 적극적인 표준화 활동을 통한 IoT 표준의 도출에 나서고 있다. 다만, 아직까지는 관련 국제 표준에 대한 표준화가 완성되지 않아 각 제조사 혹은 서비스 사업자별로 상이한 기술을 적용하여 제공하고 있으며 이로 인한 상호 연동에는 제약이 존재한다. 본 고에서는 이러한 정보가전 서비스의 제어를 위한 IoT 기술의 개발 동향과 각 기기간 연동을 위한 IoT 기술의 동향을 확인한다.

• Information and Communications Magazine
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• v.33 no.3
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• pp.80-86
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• 2016

IoT 통신 환경이 구축되면서 고사양 기기뿐만 아니라 저사양 기기를 사용하는 통신도 함께 증가하고 있다. 안전한 통신을 위해서는 메시지 암호화와 인증을 함께 제공하는 블록 암호 기술이 요구된다. 하지만, 기존 블록 암호 기술을 통신, 계산 기능이 제약된 저사양 기기에 그대로 사용하기에는 어려움이 따른다. 따라서 다양한 경량 암호 기술이 등장하게 되었다. 본 논문에서는 경량 암호 기술의 동향에 대해서 살펴보고 직접 IoT 실험 기기인 8비트 아두이노, 16비트 티모트, 32비트 라즈베리 파이2를 이용하여 구현 실험한 성능 측정 결과에 대해서 논한다.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.289-303
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• 2022

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.