• Journal of The Korean Society of Integrative Medicine
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• v.7 no.2
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• pp.59-67
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• 2019

Purpose : The purpose of this study was to analyze the dynamic range of motion (ROM) of pelvic and translation of center of mass (COM) when wearing different shoe insole lifts according to leg length discrepancy (LLD) during free speed gait. Methods : Thirty-five healthy adults were participated in this study. Kinematic data were collected using a Vicon motion capture system. Reflective and cluster 40 markers attached to participants lower extremities and were asked to walk in a 6 m gait way under three different shoe lift conditions (without any insole, 1 cm insole, and 2 cm insole). The pelvic ROM and COM translation in three planes were sorted using a Nexus software, and a Visual3D motion analysis software was used to coordinate all kinematic data. Results : There were significantly increased maximal pelvic elevation and total pelvic range in coronal plane when wearing a standard shoe with 2 cm insole lift during gait (p<.05). When wearing a standard shoe with 2 cm insole lift, the total range of the pelvic segment were significantly different in all three motion planes (p<.05). Conclusion : Although LLD of less than 2 cm develops abnormal movement pattern of the pelvis and may cause of musculoskeletal diseases such as low back pain, hip and knee joint osteoarthritis, therefore intensive various physical therapy interventions for LLD are needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.514-535
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• 2019

Information-centric networks operate under the assumption that all network components have built-in caching capabilities. Integrating the caching strategies of information centric networking (ICN) with wireless virtualization improves the gain of virtual infrastructure content caching. In this paper, we propose a framework for software-defined information centric virtualized wireless device-to-device (D2D) networks. Enabling D2D communications in virtualized ICN increases the spectral efficiency due to reuse and proximity gains while the software-defined network (SDN) as a platform also simplifies the computational overhead. In this framework, we propose a joint virtual resource and cache allocation solution for latency-sensitive applications in the next-generation cellular networks. As the formulated problem is NP-hard, we design low-complexity heuristic algorithms which are intuitive and efficient. In our proposed framework, different services can share a pool of infrastructure items. We evaluate our proposed framework and algorithm through extensive simulations. The results demonstrate significant improvements in terms of visiting latency, end user QoE, InP resource utilization and MVNO utility gain.

• Smart Structures and Systems
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• v.29 no.1
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• pp.207-220
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• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• ETRI Journal
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• v.45 no.5
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• pp.887-898
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• 2023

Aerial base stations (ABSs) seem promising to enhance the coverage and capacity of fifth-generation and upcoming networks. With the flexible mobility of ABSs, they can be positioned in air to maximize the number of users served with a guaranteed quality of service (QoS). However, ABSs may be overloaded or underutilized given inefficient placement, and user association has not been well addressed. Hence, we propose a three-dimensional ABS placement scheme with a delay-QoS-driven user association to balance loading among ABSs. First, a load balancing utility function is designed based on proportional fairness. Then, an optimization problem for joint ABS placement and user association is formulated to maximize the utility function subject to statistical delay QoS requirements and ABS collision avoidance constraints. To solve this problem, we introduce an efficient modified gray wolf optimizer for ABS placement with a greedy user association strategy. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of load balancing and delay QoS provisioning.

• Archives of Reconstructive Microsurgery
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• v.9 no.2
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• pp.139-146
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• 2000

The aim of this study was to assess whether the functional regeneration of a lyophilized autografted cartilage could be improved by implanting a vascularized muscle flap into the medullary canal of autografted proximal humerus. A hemijoint reconstruction using a lyophilized osteochondral autograft in proximal humerus was done in 4 rabbits for control, and combined with an vascularized intramedullary muscle flap in another 4 rabbits for the experimental group. Graft healing and the repair process of osteochondral graft were followed by serial radiographs and histologic changes for 9 weeks after experiments. Each two rabbits in control and in experimental group on 5th and 9th week after implantation of hemijoint were sacrified. The results were as follows: 1. All of control and experimental froups on 5th week united solidly on osteotomized site radiologically, but their articular cartilages were destroyed more seriously in the control than that in experimental group with muscle flap on 5th and 9th week after experiment... 2. Histochemically, the cartilage surface are completely destroyed and revealed with severe osteoarthritic changes on all cartilage layers in control, but cartilaginous erosions are mild to moderate and their arthritic changes are also mild with somewhat regeneration of chondrocytes on deep layers more prominetly on 9th week of the experimental group. 3. The amount of collagen and protenized matrix which was determined by Masson-Trichrome stain was markedly decreased that means the weakness of bony strength and low osteogenic potential in lyophilized cartilage. These results suggest that an intramedullary vascularized muscle flap can improve the functional results of lyophilized osteochondral autograft by providing both increased vascularity and populations of mesenchymal cells to initiate new bone formation on osteotomized site as well as the regeneration of deep layers in articular cartilage. In clinical relevances, this lyophilized hemijoint autograft combined with an intramedullary vascularized muscle pedicle graft might be used very effectively for the treatment of malignant long bone tumors to preserve the joint functions, all or partly, and so to replace it with the artificial joint after tumor excision and hemijoint autograft.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.115-121
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• 2017

The present study aimed to obtain basic knowledge of a customized artificial joint based on the convergence research of Digital Imaging and Communications in Medicine(DICOM) and 5-axis machining technology. In the case of the research method, three-dimensional modeling was generated based on the medical image of the humerus bone, and the shape was machined using a chemical wood material. Then, the anatomical characteristics and the modeling machining computation times were compared. The results showed that the Stereolithography (STL) modeling required twice more time for semi-finishing and 10 times more time for finishing compared to the Initial Graphics Exchange Specification(IGES) modeling. For the 5-axis machining humerus bone, the anatomical structures of the anatomic neck, greater tubercle, lesser tubercle, and intertubercular groove were similar to those in the three-dimensional medical image. In the future, the convergence machining technology, where 5-axis machining of various structures(e.g., the surgical neck undercut of the humerus bone) is performed as described above, can be efficiently applied to the manufacture of a customized joint that pursues the precise model of a human body.

• Convergence Security Journal
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• v.20 no.4
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• pp.27-34
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• 2020

Recently, the fourth industrial revolution technology has not only changed everyday life greatly through technological development, but has also become a major keyword in the establishment of defense policy. In particular, Internet of Things, cloud, big data, mobile and cybersecurity technologies, called ICBMS, were selected as core leading technologies in defense information policy along with artificial intelligence. Amid the growing importance of the fourth industrial revolution technology, research is being carried out to develop the C4I system, which is currently operated separately by the Joint Chiefs of Staff and each military, including the KJCCS, ATCIS, KNCCS and AFCCS, into an integrated system in preparation for future warfare. This is to solve the problem of reduced interoperability for joint operations, such as information exchange, by operating the C4I system for each domain. In addition, systems such as the establishment of an integrated C4I system and the U.S. military's Risk Management Framework (RMF) are essential for efficient control and safe operation of weapons systems as they are being developed into super-connected and super-intelligent systems. Therefore, in this paper, the intelligent cyber threat detection, management of users' access to information, and intelligent management and visualization of cyber threat are presented in the future C4I system based on big data/cloud.