• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2142-2161
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• 2018

Distance dynamics model is an excellent model for uncovering the community structure of a complex network. However, the model has poor robustness. To improve the robustness, we design an enhanced distance dynamics model based on Ego-Leader and propose a corresponding community detection algorithm, called E-Attractor. The main contributions of E-Attractor are as follows. First, to get rid of sensitive parameter ${\lambda}$, Ego-Leader is introduced into the distance dynamics model to determine the influence of an exclusive neighbor on the distance. Second, based on top-k Ego-Leader, we design an enhanced distance dynamics model. In contrast to the traditional model, enhanced model has better robustness for all networks. Extensive experiments show that E-Attractor has good performance relative to several state-of-the-art algorithms.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.1-8
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• 2006

In this paper, we propose a distance-based location registration scheme and evaluate it's performance in a mobility model. We compare performance of the distance-based registration scheme to that of zone-based registration scheme at the mobility model. Numerical results show that the registration load of the distance-based registration with call arrival is similar to that of the zone-based registration, and is equally distributed to all cells in a location area. So the proposed scheme can be effectively used in the limited radio resources.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.385-401
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• 1997

In this paper, we evaluate the performance of zone-based registration and distance-based registration. First, we propose the mobility model which can be used to analyze the performance of both zone-based registration and distance-based registration. And using the proposed mobility model, we obtain several performance measures and perform numerical computation to compare the performance of two registration methods. Numerical results show that in general zone-based registration needs less number of registration than distance-based registration. On the other hand, if distance-based registration is used, registration load is equally distributed to all cells in a location area and ping-pong phenomenon is not occurred. And when a VLR area is composed of a few location areas, distance-based registration may need less registration load than zone-based registration. Therefore, a proper registration method should be selected considering system circumstances and implementation complexity, and the selected method should be implemented so as to change system parameters according to system circumstances.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.2
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• pp.79-85
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• 2015

Existing Differential GPS (DGPS) pseudorange correction (PRC) generation techniques based on a virtual reference station cannot effectively assign a weighting factor if the baseline distance between a user and a reference station is not long enough. In this study, a virtual reference station DGPS PRC generation technique was developed based on an enhanced inverse distance weighting method using an exponential function that can maximize a small baseline distance difference due to the dense arrangement of DGPS reference stations in South Korea, and its positioning performance was validated. For the performance verification, the performance of the model developed in this study (EIDW) was compared with those of typical inverse distance weighting (IDW), first- and second-order multiple linear regression analyses (Planar 1 and 2), the model of Abousalem (1996) (Ab_EXP), and the model of Kim (2013) (Kim_EXP). The model developed in the present study had a horizontal accuracy of 53 cm, and the positioning based on the second-order multiple linear regression analysis that showed the highest positioning accuracy among the existing models had a horizontal accuracy of 51 cm, indicating that they have similar levels of performance. Also, when positioning was performed using five reference stations, the horizontal accuracy of the developed model improved by 8 ~ 42% compared to those of the existing models. In particular, the bias was improved by up to 27 cm.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.289-296
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• 2022

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.932-951
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• 2021

The effectiveness of testing in Model-based Testing (MBT) for Software Product Line (SPL) can be achieved by considering fault detection in test case. The lack of fault consideration caused test case in test suite to be listed randomly. Test Case Prioritization (TCP) is one of regression techniques that is adaptively capable to detect faults as early as possible by reordering test cases based on fault detection rate. However, there is a lack of studies that measured faults in MBT for SPL. This paper proposes a Test Case Prioritization (TCP) approach based on dissimilarity and string based distance called Last Minimal for Local Maximal Distance (LM-LMD) with Dice-Jaro-Winkler Dissimilarity. LM-LMD with Dice-Jaro-Winkler Dissimilarity adopts Local Maximum Distance as the prioritization algorithm and Dice-Jaro-Winkler similarity measure to evaluate distance among test cases. This work is based on the test case generated from statechart in Software Product Line (SPL) domain context. Our results are promising as LM-LMD with Dice-Jaro-Winkler Dissimilarity outperformed the original Local Maximum Distance, Global Maximum Distance and Enhanced All-yes Configuration algorithm in terms of Average Fault Detection Rate (APFD) and average prioritization time.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.190-201
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• 2024

This study examined the effect of the distance Infection Control Education Program (ICEP), developed based on the ADDIE model, on infection control knowledge, attitude, and performance among care workers in long-term care facilities nationwide. The program, developed based on the ADDIE model, was applied to 173 care workers directly responsible for nursing care of elderly residents in lomg-term care facilities. The distance ICEP for care workers was conducted through the website and lasted 30 minutes for each of the eight topics. To determine the effectiveness of the education, infection control knowledge, attitude, performance, and satisfaction were surveyed before and four weeks after the program. Differences in infection control knowledge, attitude, and performance before and after the distance ICEP were assessed by a t-test. A significant difference was observed in knowledge and infection control performance after the distance ICEP was administered to care workers. In the sub-domains of infection control performance, overall understanding of infection, regular infection control education, infection control by special pathogen (multidrug-resistant bacteria, tuberculosis, tick-borne infectious diseases), and detailed infection control education by infection site (pressure ulcers and urinary tract infections) were significantly improved. Infection control knowledge and performance improved through the distance ICEP applied to care workers. Satisfaction also displayed high scores on most items and indicated that it was helpful for infection control in facilities, confirming the effectiveness of infection control education. Based on the survey of care workers nationwide, the infection education program can be effectively used for care workers in the future.

• Communications for Statistical Applications and Methods
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• v.20 no.1
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• pp.23-28
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• 2013

We propose a version of Cook's distance (called local distance) in the multivariate linear model. The proposed version is a matrix, while the existing version of Cook's distance (called global distance) is a scalar. The existing Cook's distance is the trace of the proposed Cook's distance. In addition, we argue that the proposed Cook's distance has a more natural extension of the Cook's distance in the univariate linear model than the existing Cook's distance. An illustrative example based on a real data set is given.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.298-304
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• 2021

Currently, to prevent the spread of COVID-19 virus infection, gathering of more than 5 people in the same space is prohibited. The purpose of this paper is to measure the distance between objects using the Yolov5 model for processing real-time images with OpenCV in order to restrict the distance between several people in the same space. Also, Utilize Euclidean distance calculation method in DeepSORT and OpenCV to minimize occlusion. In this paper, to detect the distance between people, using the open-source COCO dataset is used for learning. The technique used here is using the YoloV5 model to measure the distance, utilizing DeepSORT and Euclidean techniques to minimize occlusion, and the method of expressing through visualization with OpenCV to measure the distance between objects is used. Because of this paper, the proposed distance measurement method showed good results for an image with perspective taken from a higher position than the object in order to calculate the distance between objects by calculating the y-axis of the image.