• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.479-486
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• 2010

Reliable hydrologic data acquisition is the basic and essential requirement for efficient water management. Especially the acquisition of various stream data in a certain location is very important to construct on alarm system to response an urban flood which occurs frequently due to the effect of climate change. Although the frequency of stream inundation flood occurrence becomes low owing to the consistent stream improvement, the urban flood due to the drainage system problems such as deterioration and bad management occurs continuously. The consistent management and current status understanding of the urban drainage system is essential to reduce the urban flood. The purpose of this study is to develop the urban runoff network flow velocity monitoring system which has the capability of collecting stream data whenever, wherever and to whomever without expert knowledge using Code Division Multiple Access technique and Bluetooth near-distance wireless communication technique. The urban runoff network flow velocity monitoring system consists of three stages. In the first stage, the stream information obtained by using ubiquitous floater is transferred to the server computer. In the second stage, the current state of the urban drainage system is assessed through the server computer. In the last stage, the information is provided to the user through a GUI. As a result of applying, the developed urban runoff network flow velocity monitoring system to Woncheon-Stream in Suwon, the information necessary for urban drainage management can be managed in real time.

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.1-9
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• 2008

Fuzzy Cognitive Map, one of ways to model, describe and infer reasoning relations, is widely used in the field of reasoning knowledge engineering. Despite of the natural and easy understanding of decision and smooth explanation of relation between front and rear, reasoning relation is organized with mathematical haziness and complex algorithm and rarely has an interactive user interface. This paper suggests an interactive Fuzzy Cognitive Map(FCM) construction support system. It builds a FCM increasingly concerning multiple experts' knowledge. Futhermore, it supports user-supportive environment by dynamically displaying the structure of Fuzzy Cognitive Map which is constructed by the interaction between experts and the system.

• Journal of Internet Computing and Services
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• v.16 no.4
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• pp.25-37
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• 2015

This study has developed a mobile AR contents Builder (MARB) featuring the augmentation of virtual objects, e. g. 2D images, 3D models and videos on user-preferred markers in mobile environment and with connected interaction events. The developed MARB is capable of augmenting multiple virtual objects on markers preferred by users, and selectively supporting many marker-specific interaction events. The MARB is also developed as a mobile App for convenience of use in the mobile environment. This makes users to implement AR contents by a GUI-based authoring procedure on the mobile device. The developed MARB allows App developers and general users to author mobile contents easily and can be applied to various field by those who wish to use diverse mobile AR contents. Moreover, the MARB will provide solutions for the expansion of mobile AR market, by overcoming the difficulties of mobile AR content development.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.239-247
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• 2012

This paper is related to an Object VR-based virtual textile wearing system carrying out textile texture mapping based on viewpoint vector estimation and intensity difference map. The proposed system is characterized as capable of virtually wearing a new textile pattern selected by the user to the clothing shape section segmented from multi-view 2D images of clothes model for Object VR(Object Virtual Reality), and three-dimensionally viewing its virtual wearing appearance at multi-view points of the object. Regardless of color or intensity of model clothes, the proposed system is possible to virtually change the textile pattern with holding the properties of the selected clothing shape section, and also to quickly and easily simulate, compare, and select multiple textile pattern combinations for individual styles or entire outfits. The proposed system can provide higher practicality and easy-to-use interface, as it makes real-time processing possible in various digital environment, and creates comparatively natural and realistic virtual wearing styles, and also makes semi-automatic processing possible to reduce the manual works.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.194-199
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• 2019

As the performance of the system increases, more parallelized data is being processed than single processing of data. Today's cpu structure has been developed to leverage multicore, and hence data processing methods are being developed to enable parallel processing. In recent years desktop cpu has increased multicore, data is growing exponentially, and there is also a growing need for data processing as artificial intelligence develops. This neural network of artificial intelligence consists of a matrix, making it advantageous for parallel processing. This paper aims to speed up the processing of the system by using raspberrypi to implement the cluster building and parallel processing system against the backdrop of the foregoing discussion. Raspberrypi is a credit card-sized single computer made by the raspberrypi Foundation in England, developed for education in schools and developing countries. It is cheap and easy to get the information you need because many people use it. Distributed processing systems should be supported by programs that connected multiple computers in parallel and operate on a built-in system. RaspberryPi is connected to switchhub, each connected raspberrypi communicates using the internal network, and internally implements parallel processing using the Message Passing Interface (MPI). Parallel processing programs can be programmed in python and can also use C or Fortran. The system was tested for parallel processing as a result of multiplying the two-dimensional arrangement of 10000 size by 0.1. Tests have shown a reduction in computational time and that parallelism can be reduced to the maximum number of cores in the system. The systems in this paper are manufactured on a Linux-based single computer and are thought to require testing on systems in different environments.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.151-158
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• 2014

In this paper, we have designed and implemented the 'N-screen based solar power monitoring system' using N-screen technology by improving the existing monitoring system that can only be accessed or controlled by PC. The HW of the system is consists of solar power modules, inverter, and relay server. And SW is implemented in consideration of the following three points. First, we applied the N-screen technology in order to support various devices. Second, we applied 1:N communication technology that multiple devices can be connected simultaneously to the relay server. Third, we also considered the security problems between server and devices. The final system has been evaluated through the operational test and received good reviews from the technical and economic point. In the future, we are expecting that this technology will replace the existing monitoring systems in various fields, like smart home, smart building, smart city, smart industry, and smart agriculture, etc.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.5
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• pp.303-310
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• 2017

Underwater robots operating in constrained underwater environment have requirements for software systems. Firstly, it is necessary to provide reusable common software components for hardware interface of sensors and actuators that are frequently used in underwater robots. Secondly, it is required to support distributed execution environment on multiple embedded controllers. Thirdly, it is need to implement a monitoring system capable of high-speed and large-data transmission for underwater robots operating in an environment where it is difficult to check the robot status. For these requirements, we have designed the layered architecture pattern and applied several design patterns to enhance the reusability and the maintainability of software components, In addition, we overlaid the broker architecture pattern to support distributed execution environments. Finally, we implemented the underwater robot software system using ROS framework based on the software architecture design. In order to evaluate the performance of the implemented software system, we performed an experiment to measure the response time between components and the transmission rate of the monitoring data, and obtained the results satisfying the required performance.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.11
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• pp.471-478
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• 2014

Database replication is utilized to increase credibility, availability and prevent overload of distributed databases. Two models currently exist for replication - Master/Slave and Multi-Master. Since the Multi-Master model has problems of increasing complexity and costs to interface among multiple databases for updates and inserts, the Master/Slave model is more appropriate when frequent data inserts and updates are required. However, Master/Slave model also has a problem of not having exact criteria when systems choose to connect between Master and Slave for transactions. Therefore, this research suggests a routing algorithm based on AOP (Aspect Oriented Programming) in the Master/Slave database model. The algorithm classifies applications as cross-cutting concerns based on AOP, modularizes each concern, and routes transactions among Master and Slave databases. This paper evaluates stability and performance of the suggested algorithm through integration tests based on scenarios.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.8
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• pp.746-752
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• 2009

In this paper, we deal with the development of an optimal design program for a triple-band PTFA(Planar Inverted-F Antenna) of 433 MHz, 912 MHz and 2.45 GHz by using evolution strategy. Generally, the resonance frequency of the PIFA is determined by the width and length of a U-type slot used. However the resonance frequencies of the multiple U slots are varied by the mutual effect of the slots. Thus the optimal width and length of U-type slots are determined by using an optimal design program based on the evolution strategy. To achieve this, an interface program between a commercial EM analysis tool and the optimal design program is constructed for implementing the evolution strategy technique that seeks a global optimum of the objective function through the iterative design process consisting of variation and reproduction. The resonance frequencies of the triple-band PIFA yielded by the optimal design program are 430 MHz, 910.5 MHz and 2.458 GHz that show a good agreement to the design target values.