• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.6
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• pp.2604-2611
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• 2013

The Personal Rapid Transit(PRT) system has been highly interested in future transportation developments due to its on-demand and optimized door-to-door transport capability. However, the major impediments to the commercialization of PRT are the high cost for construction of infrastructures as opposed to the small transport capacity and difficulty in defining the role of PRT in building a balanced transportation system. In this study, the vertical transfer device for the PRT vehicle is developed to provide more flexible and better compatible urban mobility services between means of transportation, which is expected to meet particular demands in a particular environment. This apparatus was initially designed based on the basis of vertical circulating conveyors with steel chains, which is frequently used in logistics. Its advantages are capable of the non-stop loading and reduced head-way time. Most importantly, it was intensified by the additional idea to ensure the stable and reliable transfer of the PRT vehicle fully loaded with passengers. The 1/10-scale prototype was successfully tested to demonstrate a fundamental mechanism of vertical transfer and identify unexpected user requirements prior to a real manufacturing process.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.313-326
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• 2018

This paper introduces core techniques on ship detection and tracking based on a compact HF radar platform which is necessary to establish a wide-area surveillance network. Currently, most HF radar sites are primarily optimized for observing sea surface radial velocities and bearings. Therefore, many ship detection systems are vulnerable to error sources such as environmental noise and clutter when they are applied to these practical surface current observation purpose systems. In addition, due to Korea's geographical features, only compact HF radars which generates non-uniform antenna response and has no information on target information are applicable. The ship detection and tracking techniques discussed in this paper considers these practical conditions and were evaluated by real data collected from the Yellow Sea, Korea. The proposed method is composed of two parts. In the first part, ship detection, a constant false alarm rate based detector was applied and was enhanced by a PCA subspace decomposition method which reduces noise. To merge multiple detections originated from a single target due to the Doppler effect during long CPIs, a clustering method was applied. Finally, data association framework eliminates false detections by considering ship maneuvering over time. According to evaluation results, it is claimed that the proposed method produces satisfactory results within certain ranges.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.578-585
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• 2012

Location tracking system through GPS and Wi-Fi is available at no additional cost in an environment of IEEE 802.11-based wireless network. It is useful for many applications in outdoor environment. However, a previous systems used for general device to tag. It is unsuitable for power aware location tracking system because general devices is more expensive and non-optimized for tracking. The hand-off method of IEEE 802.11 standard is not enough considering power consumption. This thesis analyzes the previous location tracking systems and proposes power aware system. First, we designed and implemented tag to optimize location tracking. Next, we propose low-power hand-off method and low-power behavior model in implemented tag. The proposed hand-off method resolve power problem by using the location information and behavior model minimize power consumption of tag through power-saving mode and the concept of duty cycle. To evaluating proposed methods and system performance, we perform simulations and experiments in real environment. And then, we calculate tag's power consumption based on the actual measured current consumption of each operation. In a simulation result, the proposed behavior model and hand-off method reduced about 98%, 59% than the standard's hand-off and default behavior model.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1900-1906
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• 2008

According as zone of life of big city is expanded by new city development etc.., need special skill master plan compensation which can be systematic for wide area traffic problem solution and confusion expense minimization in the metropolitan area because wide area of capital region traffic is gone continuously and continues. Accordingly, Kyonggi Province suggested rapid-transit railway achievement that can connect Seoul Gangnam in 20 minutes with Dongtan new city recently. MLTM(Ministry of Land Transport and Maritime Affaris) announced "Capital region railway network improvement plan research services (2007.12)" result that Gangnam High-speed railway route (Suse $\sim$ Dongtan $\sim$ Pyeongtaek) construction for offer High-speed railway service to capital region and Kyonggi southern part area inhabitantses and need to line capacity tribe solution by Seoul-Busan high-speed railway second-stage and Honam high speed railroad completion. Is judged that need examination about wide area rapid-transit railway combined application operation that take advantage of rail track reserve capacity of High-speed railway for utilization efficiency elevation of country and efficient investment of national finance according as High-speed railway and Gangnam rapid-transit railway route that is suggested in Kyonggi Province are overlaped. Therefore, in this research, I wish to quote investment efficiency plan of railroad business by that different kind's train is run in uniformity track by presenting combined application operation plan and working expenses curtailment effect etc. that is optimized through analysis of roadbed and E & M technology condition, intermediate station plan, train operation planning etc. in case of wide area rapid-transit railway and high-speed railway run combined application.

• Advances in nano research
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• v.13 no.6
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• pp.575-585
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• 2022

Due to its biofilm formation and colonization of the osteocyte-lacuno canalicular network (OLCN), Staphylococcus aureus (S.aureus) implant-associated bone infection (SIABI) is difficult to cure thoroughly, and may occur recurrently subsequently after a long period dormant. It is essential to explore an alternative therapeutic strategy that can eradicate the pathogens in the infected foci. To address this, the polymethylmethacrylate (PMMA) bone cement and Fe3O4 nanoparticles compound cylinder were developed as implants based on their size and mechanical properties for the alternative magnetic field (AMF) induced thermal ablation, The PMMA mixed with optimized 2% Fe₃O₄ nanoparticles showed an excellent antibacterial efficacy in vitro. It was evaluated by the CFU, CT scan and histopathological staining on a rabbit 1-stage transtibial screw model. The results showed that on week 7, the CFU of infected soft tissue and implants, and the white blood cells (WBCs) of the PMMA+2% Fe₃O₄+AMF group decreased significantly from their controls (p<0.05). PMMA+2% Fe₃O₄+AMF group did not observe bone resorption, periosteal reaction, and infectious reactive bone formation by CT images. Further histopathological H&E and Gram Staining confirmed there was no obvious inflammatory cell infiltration, neither pathogens residue nor noticeably burn damage around the infected screw channel in the PMMA+2% Fe₃O₄+AMF group. Further investigation of nanoparticle distributions in bone marrow medullary and vital organs of heart, liver, spleen, lung, and kidney. There were no significantly extra Fe₃O₄ nanoparticles were observed in the medullary cavity and all vital organs either. In the current study, PMMA+2% Fe₃O₄+AMF shows promising therapeutic potential for SIABI by providing excellent mechanical support, and promising efficacy of eradicating the residual pathogenic bacteria in bone infected lesions.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.305-316
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• 2022

With the flood of digital data owing to the Internet of Things and big data, cloud service providers that process and store vast amount of data from multiple users can apply duplicate data elimination technique for efficient data management. The user experience can be improved as the notion of edge computing paradigm is introduced as an extension of the cloud computing to improve problems such as network congestion to a central cloud server and reduced computational efficiency. However, the addition of a new edge device that is not entirely reliable in the edge computing may cause increase in the computational complexity for additional cryptographic operations to preserve data privacy in duplicate identification and elimination process. In this paper, we propose an efficiency-improved duplicate data elimination protocol while preserving data privacy with an optimized user-edge-cloud communication framework by utilizing a trusted execution environment. Direct sharing of secret information between the user and the central cloud server can minimize the computational complexity in edge devices and enables the use of efficient encryption algorithms at the side of cloud service providers. Users also improve the user experience by offloading data to edge devices, enabling duplicate elimination and independent activity. Through experiments, efficiency of the proposed scheme has been analyzed such as up to 78x improvements in computation during data outsourcing process compared to the previous study which does not exploit trusted execution environment in edge computing architecture.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.827-846
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• 2023

Recently, various information technologies such as network communication and sensors have begun to be integrated into weapon systems that were previously operated in stand-alone. This helps the operators of the weapon system to make quick and accurate decisions, thereby allowing for effective operation of the weapon system. However, as the involvement of the cyber domain in weapon systems increases, it is expected that the potential for damage from cyber attacks will also increase. To develop a secure weapon system, it is necessary to implement built-in security, which helps considering security from the requirement stage of the software development process. The U.S. Department of Defense is implementing the Risk Management Framework Assessment and Authorization (RMF A&A) process, along with the introduction of the concept of cybersecurity, for the evaluation and acquisition of weapon systems. Similarly, South Korea is also continuously making efforts to implement the Korea Risk Management Framework (K-RMF). However, so far, there are no cases where K-RMF has been applied from the development stage, and most of the data and documents related to the U.S. RMF A&A are not disclosed for confidentiality reasons. In this study, we propose the method for inferring the composition of the K-RMF based on systematic threat analysis method and the publicly released documents and data related to RMF. Furthermore, we demonstrate the effectiveness of our inferring method by applying it to the naval battleship system.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.91-98
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• 2023

Recently, according to development of artificial intelligence, a wide range of industry being automatic and optimized. Also we can find out some research of using supervised learning for deteceting defect of railway in domestic rail industry. However, there are structures other than rails on the track, and the fastener is a device that binds the rail to other structures, and periodic inspections are required to prevent safety accidents. In this paper, we present a method of reducing cost for labeling using semi-supervised and transfer model trained on rail fastener data. We use Resnet50 as the backbone network pretrained on ImageNet. At first we randomly take training data from unlabeled data and then labeled that data to train model. After predict unlabeled data by trained model, we adopted a method of adding the data with the highest probability for each class to the training data by a predetermined size. Futhermore, we also conducted some experiments to investigate the influence of the number of initially labeled data. As a result of the experiment, model reaches 92% accuracy which has a performance difference of around 5% compared to supervised learning. This is expected to improve the performance of the classifier by using relatively few labels without additional labeling processes through the proposed method.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.949-958
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• 2022

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion: Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.