• Proceedings of the KSME Conference
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• 2008.11a
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• pp.664-667
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• 2008

Since the advent of pedicle screw fixation system, posterior spinal fusion has markedly increased This intemal fixation system has been reported to enhance the fusion rates, thereby becoming very popular procedure in posterior spinal arthrodesis. Although some previous studies have shown the complications of spinal instruments removal, i.e. loss of correction and spinal collapse in scoliosis or long spine fusion patients, there has been no study describing the benefit or complications in lumbar spinal fusion surgery of one or two level. In order to clarify the effect of removal of instruments on mechanical motion profile, we simulated a finite element model of instrumented posterolateral fused lumbar spine model, and investigated the change of mechanical motion profiles after the removal of instrumentation.

• Journal of Korean Society of Transportation
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• v.23 no.5 s.83
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• pp.135-146
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• 2005

Up to now studies on the fusion of travel time from various detectors have been conducted based on the variance raito of the intermittent data mainly collected by GPS or probe vehicles. The fusion model based on the variance ratio of intermittent data is not suitable for the license plate recognition AVIs which can deal with vast amount of data. This study was carried out to develop the fusion model based on travel time acquired from the license plate recognition AVIs and the point detectors. In order to fuse travel time acquired from the point detectors and the license plate recognition AVIs, the optimized fusion model and the proportional fusion model were developed in this study. As a result of verification, the optimized fusion model showed the superior estimation performance. The optimized fusion model is the dynamic fusion ratio estimation model on real time base, which calculates fusion weights based on real time historic data and applies them to the current time period. The results of this study are expected to be used effectively for National Highway Traffic Management System to provide traffic information in the future. However, there should be further studies on the Proper distance for the establishment of the AVIs and the license plate matching rate according to the lanes for AVIs to be established.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.5
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• pp.577-584
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• 2009

Fusion of decisions in wireless sensor networks having flexibility on energy efficiency is studied in this paper. Two representative distributions, the generalized Gaussian and $\alpha$-stable probability density functions, are used to model non-Gaussian noise channels. By incorporating noise channels into the parallel fusion model, the optimal fusion rules are represented and suboptimal fusion rules are derived by using a large signal-to-noise ratio(SNR) approximation. For both distributions, the obtained suboptimal fusion rules are same and have equivalent form to the Chair-Varshney fusion rule(CVR). Thus, the CVR does not depend on the behavior of noise distributions that belong to the generalized Gaussian and $\alpha$-stable probability density functions. The simulation results show the suboptimality of the CVR at large SNRs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.1194-1211
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• 2011

The main research purpose of this paper is developed to Fusion Management System Model that is competition advantage and excellence airport for the airport authorities. Fusion Management System Model is developed to fuse and convert how is consist of creativity management and management quality system. Fusion management system structure is as follow. Firstly, part of foundation configured to philosophy and intention of CEO, company as like university and value-based creative corporate culture. Secondly, part of body is applied that is process by 6sigma, system by ISO9001. Thirdly, part of head is applied that is strategy and evaluation by Malcolm Baldrige National Quality Award of U.S.A. Also, system connected to synergy line that is creativity management, fusion & convergence and trust. Finally, this system has been researched to the practical perspective in order to realize for airport authorities.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.130-136
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• 2010

For stability analysis of the lumbar spine, the hypothesis presented is that the disc has stress sensors driving feedback mechanism, which could react to the imposed loads by adjusting the contraction of the muscles. Fusion in the motion segment of the lumbar spinal column is believed to alter the stability of the spinal column. To identify this effect finite element (FE) models combined with optimization technique was applied and quantify the role of each muscle and reaction forces in the spinal column with respect to the fusion level. The musculoskeletal FE model was consisted with detailed whole lumbar spine, pelvis, sacrum, coccyx and simplified trunk model. Vertebral body and pelvis were modeled as a rigid body and the rib cage was constructed with rigid truss element for the computational efficiency. Spinal fusion model was applied to L3-L4, L4-L5, L5-S1 (single level) and L3-L5 (two levels) segments. Muscle architecture with 46 local muscles was used as acting directions. Minimization of the nucleus pressure deviation and annulus fiber average axial stress deviation was selected for cost function. As a result, spinal fusion produced reaction changes at each motion segment as well as contribution of each muscle. Longissimus thoracis and psoas major muscle showed dramatic changes for the cases of L5-S1 and L3-L5 level fusion. Muscle force change at each muscle also generated relatively high nucleus pressure not only at the adjacent level but at another level, which can explain disc degeneration pattern observed in clinical study.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Smart Structures and Systems
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• v.11 no.6
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• pp.569-587
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• 2013

This paper evaluates the closed loop performance of the reaching law based discrete sliding mode controller with multisensor data fusion (MSDF) in real time, by controlling the first two vibrating modes of a piezo actuated structure. The vibration is measured using two homogeneous piezo sensors. The states estimated from sensors output are fused. Four fusion algorithms are considered, whose output is used to control the structural vibration. The controller is designed using a model identified through linear Recursive Least Square (RLS) method, based on ARX model. Improved vibration suppression is achieved with fused data as compared to single sensor. The experimental evaluation of the closed loop performance of sliding mode controller with data fusion applied to piezo actuated structure is the contribution in this work.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.