• Journal of Korean Society of Transportation
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• v.16 no.2
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• pp.177-195
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• 1998

지능형교통체계(ITS:Intellegent Transport System)의 구현을 위한 가장 중요한 요소중의 하나는 교통정보의 생성이다. 교통정보의 생성은 루프 검지기, 폐쇄회로(CCTV), probe 차량, 경찰, 통신원 등을 수집된 제보자료들을 분석 및 가공함으로써 이루어진다. 그러나 이들 수집원은 주어진 시간에 있어 모든 네트웍을 통해서 자료가 완전히 수집되어지는 것은 아니다. 즉, 특정 지역에 수집원이 몰려 있는 경우가 있는 반면, 전혀 수집되어지지 않는 지역이 발생할 수도 있다. 이러한 공간적인 불균형적 특성은 동시에 발생한 다량의 자료를 처리하는 기술과 자료가 수집되지 않은 지역에 대한 처리기술을 요하게 된다. 본 논문은 전술한 바와 같은 사항에 대하여 ITS의 진행 단계별로 드러날 수 있는 문제점을 검토하고, 자료통합에 대한 일반적인 개념을 우선 설명한다. 다음에 특정시각에 주어진 자료의 통합을 위해 퍼지선형회귀모형(fuzzy linear regression model)과 데이터 퓨전(data fusion)기법의 내용을 소개하고, 신뢰성있는 단일 교통정보생성을 위한 테이터 퓨전 알고리즘을 제시한다. 또한 제시된 알고리즘을 토대로 가상의 자료를 이용하여 적용가능 봉? 타진해 보았다. 제시되어진 알고리즘은 향후 교통정보 수집환경이 어느 정도 형성된다고 볼 때, 예측치와 실측자료간의 자료검증을 통하여 신뢰도를 가질 경우 보다 광범위하게 사용되어질 수 있을 것으로 판단된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.913-916
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• 2008

다중 센서 데이터 융합(Multi-Sensor Data Fusion)에 기반하여 자율관리 기능을 갖는 상황인식시스템에 대해 연구하였다. 다양한 형태의 센서들이 대규모의 네트워크로 연결된 환경에서 센서로부터 실시간으로 입력되는 데이터들을 융합하여 상황인식처리를 수행하는 시스템으로 노드에 설치된 소프트웨어 콤포넌트의 이상 유무를 자동 감지하고 치료하는 자율관리(Autonomic management) 기능을 갖는다. 제안한 시스템은 유비쿼터스 및 국방 무기체계의 감시·정찰, 지능형 자율 로봇, 지능형 자동차 등 다양한 상황인식 시스템에 적용가능하다.

• Journal of Korean Neurosurgical Society
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• v.67 no.3
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• pp.354-363
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• 2024

Objective : This study aims to determine the optimal dose of recombinant-human bone morphogenic protein-2 (rhBMP-2) for successful bone fusion in minimally invasive lateral lumbar interbody fusion (MIS LLIF). Previous studies show that rhBMP is an effective alternative to autologous iliac crest bone graft, but the optimal dose remains uncertain. The study analyzes the fusion rates associated with different rhBMP doses to provide a recommendation for the optimal dose in MIS LLIF. Methods : Ninety-three patients underwent MIS LLIF using demineralized bone matrix (DBM) or a mixture of rhBMP-2 and DBM as fusion material. The group was divided into the following three groups according to the rhBMP-2 usage : group A, only DBM was used (n=27); group B, 1 mg of rhBMP-2 per 5 mL of DBM paste (n=41); and group C, 2 mg of rhBMP-2 per 5 mL of DBM paste (n=25). Demographic data, clinical outcomes, postoperative complication and fusion were assessed. Results : At 12 months post-surgery, the overall fusion rate was 92.3% according to Bridwell fusion grading system. Groups B and C, who received rhBMP-2, had significantly higher fusion rates than group A, who received only DBM. However, there was no significant increase in fusion rate when the rhBMP-2 dosage was increased from group B to group C. The groups B and C showed significant improvement in back pain and Oswestry disability index compared to the group A. The incidence of screw loosening was decreased in groups B and C, but there was no significant difference in the occurrence of other complications. Conclusion : Usage of rhBMP-2 in LLIF surgery leads to early and increased final fusion rates, which can result in faster pain relief and return to daily activities for patients. The benefits of using rhBMP-2 were not significantly different between the groups that received 1 mg/5 mL and 2 mg/5 mL of rhBMP-2. Therefore, it is recommended to use 1 mg of rhBMP-2 with 5 mL of DBM, taking both economic and clinical aspects into consideration.

• Journal of Mechanical Science and Technology
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• v.16 no.10
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• pp.1253-1263
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• 2002

The control system design and modeling of an unmanned vehicle by means of a new concept for better performance through a tole-operation system is suggested by sensor fusion. But, the control of a real vehicle is very difficult, because the system identification of the vehicle is hard to find the unknown factors and the disturbances of the experimental environment. For the longitudinal and lateral controls, the traction system and steering system models are set up and a tuning method to find the gain of the controller by experiments is presented. In this research, mechanical and electronic parts are implemented to operate the unmanned vehicle and data reconstruction method of information about the environment data coming from several sensors is presented by data plot for the vehicle navigation. This paper focuses on the integration of tole-operated unmanned vehicle. This vehicle mainly controlled lateral and longitudinal directions with actuators for controlling vehicle movement and sensors for the closed-loop controlled system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.301-308
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• 2021

It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.345-352
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• 2022

Conducting AI-based fusion business due to the increment of ICT fusion medical device has been expanded. In addition, AI-based medical devices help change existing medical system on treatment into the paradigm of customized treatment such as preliminary diagnosis and prevention. It will be generally promoted to the change of medical device industry. Although the current demand forecasting of medical biotechnology commercialization is based on the method of Delphi and AHP, there is a problem that it is difficult to have a generalization due to fluctuation results according to a pool of participants. Therefore, the purpose of the paper is to predict demand forecasting for identifying promising technology based on building up big data in medical biotechnology. The development method is to employ candidate technologies of keywords extracted from SCOPUS and to use word2vec for drawing analysis indicator, technological distance similarity, and recommended technological similarity of top-level items in order to achieve a reasonable result. In addition, the method builds up academic big data for 5 years (2016-2020) in order to commercialize technology excavation on demand perspective. Lastly, the paper employs global data studies in order to develop domestic and international demand for technology excavation in the medical biotechnology field.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.53-61
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• 2014

For monitoring the flight trajectory and the status of a launch vehicle, the mission control system in NARO space center process data acquired from the ground tracking system, which consists of two tracking radars, four telemetry stations, and one electro-optical tracking system. Each tracking unit exhibits its own tracking error mainly due to multi-path, clutter and radio refraction, and by utilizing only one among transmitted informations, it is not possible to determine the actual vehicle trajectory. This paper presents a way of generating flight trajectory via post-processing the data received from the ground tracking system. The post-processing algorithm is divided into two parts: compensation for atmosphere radio refraction and multi-sensor fusion, for which a decentralized Kalman filter was adopted and implemented based on constant acceleration model. Applications of the present scheme to real data resulted in the flight trajectory where the tracking errors were minimized than done by any one sensor.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.800-808
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• 2008

This paper presents a localization algorithm of the outdoor wheeled mobile robot using the sensor fusion method based on indirect Kalman filter(IKF). The wheeled mobile robot considered with in this paper is approximated to the two wheeled mobile robot. The mobile robot has the IMU and encoder sensor for inertia positioning system and GPS. Because the IMU and encoder sensor have bias errors, divergence of the estimated position from the measured data can occur when the mobile robot moves for a long time. Because of many natural and artificial conditions (i.e. atmosphere or GPS body itself), GPS has the maximum error about $10{\sim}20m$ when the mobile robot moves for a short time. Thus, the fusion algorithm of IMU, encoder sensor and GPS is needed. For the sensor fusion algorithm, we use IKF that estimates the errors of the position of the mobile robot. IKF proposed in this paper can be used other autonomous agents (i.e. UAV, UGV) because IKF in this paper use the position errors of the mobile robot. We can show the stability of the proposed sensor fusion method, using the fact that the covariance of error state of the IKF is bounded. To evaluate the performance of proposed algorithm, simulation and experimental results of IKF for the position(x-axis position, y-axis position, and yaw angle) of the outdoor wheeled mobile robot are presented.

• Journal of Korean Neurosurgical Society
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• v.62 no.1
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• pp.83-89
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• 2019

Objective : This study is a retrospective cost-benefit analysis of cervical anterior interbody fusion and cervical artificial disc replacement, which are the main surgical methods to treat degenerative cervical disc disease. Methods : We analyzed 156 patients who underwent anterior cervical disc fusion and cervical artificial disc replacement from January 1, 2008 to December 31, 2009, diagnosed with degenerative cervical disc disorder. In this study, the costs and benefits were analyzed by using quality adjusted life year (QALY) as the outcome index for patients undergoing surgery, and a Markov model was used for the analysis. Only direct medical costs were included in the analysis; indirect medical costs were excluded. Data were analyzed with TreeAge Pro $2015^{TM}$ (TreeAge Software, Inc, Williamstown, MA, USA). Results : Patients who underwent cervical anterior fusion had a total cost of KRW 2501807/USD 2357 over 5 years and obtained a utility of 3.72 QALY. Patients who underwent cervical artificial disc replacement received 4.18 QALY for a total of KRW 3685949/USD 3473 over 5 years. The cumulative cost-effectiveness ratio of cervical spine replacement surgery was KRW 2549511/QALY (USD 2402/QALY), which was lower than the general Korean payment standard. Conclusion : Both cervical anterior fusion and cervical artificial disc replacement are cost-effective treatments for patients with degenerative cervical disc disease. Cervical artificial disc replacement may be an effective alternative to obtain more benefits.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.36-54
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• 2017

The purpose of this paper is to develop a multi-sensor fusion-based traffic information acquisition system with robust to environmental changes. it combines the characteristics of each sensor and is more robust to the environmental changes than the video detector. Moreover, it is not affected by the time of day and night, and has less maintenance cost than the inductive-loop traffic detector. This is accomplished by synthesizing object tracking informations based on a radar, vehicle classification informations based on a video detector and reliable object detections of a infrared range finder. To prove the effectiveness of the proposed system, I conducted experiments for 6 hours over 5 days of the daytime and early evening on the pedestrian - accessible road. According to the experimental results, it has 88.7% classification accuracy and 95.5% vehicle detection rate. If the parameters of this system is optimized to adapt to the experimental environment changes, it is expected that it will contribute to the advancement of ITS.