• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.611-617
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• 2023

The cervical spine plays a critical role in supporting the skull, maintaining horizontal gaze, and facilitating walking. Its unique characteristics, including the widest range of motion among spinal segments, have led to extensive research on cervical sagittal alignment. Various parameters have been proposed to evaluate cervical alignment, with studies investigating their clinical significance, correlation with symptoms, and implications for surgical interventions. Recent findings suggest that cervical sagittal alignment not only impacts the cervical spine but also influences global spine-pelvic alignment through compensatory mechanisms. This comprehensive review examines classical and new parameters of cervical sagittal alignment and considers the dynamic and muscular factors associated with it.

• Journal of Animal Environmental Science
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• v.18 no.sup
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• pp.81-90
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• 2012

This study was evaluated high end research grade Near infrared spectrophotometer (NIRS) to low end popular field grade multiple Near infrared spectrophotometer (NIRS) for rapid analysis at forage quality at sight with 241 samples of Italian ryegrass silage during 3 years collected whole country for evaluate accuracy and precision between instruments. Firstly collected and build database high end research grade NIRS using with Unity Scientific Model 2500X (650 nm~2,500 nm) then trim and fit to low end popular field grade NIRS with Unity Scientific Model 1400 (1,400 nm~2,400 nm) then build and create calibration, transfer calibration with special transfer algorithm. The result between instruments was 0.000%~0.343% differences, rapidly analysis for chemical constituents, NDF, ADF, and crude protein, crude ash and fermentation parameter such as moisture, pH and lactic acid, finally forage quality parameter, TDN, DMI, RFV within 5 minutes at sight and the result equivalent with laboratory data. Nevertheless during 3 years collected samples for build calibration was organic samples that make differentiate by local or yearly bases etc. This strongly suggest population evaluation technique needed and constantly update calibration and maintenance calibration to proper handling database accumulation and spread out by knowledgable control laboratory analysis and reflect calibration update such as powerful control center needed for long lasting usage of forage analysis with NIRS at sight. Especially the agriculture products such as forage will continuously changes that made easily find out the changes and update routinely, if not near future NIRS was worthless due to those changes. Many research related NIRS was shortly study not long term study that made not well using NIRS, so the system needed check simple and instantly using with local language supported signal methods Global Distance (GD) and Neighbour Distance (ND) algorithm. Finally the multiple popular field grades instruments should be the same results not only between research grade instruments but also between multiple popular field grade instruments that needed easily transfer calibration and maintenance between instruments via internet networking techniques.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.3-11
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• 2014

Recently, rapid urbanization has necessitated continuous updates in digital map to provide the latest and accurate information for users. However, conventional aerial photogrammetry has some restrictions on periodic updates of small areas due to high cost, and as-built drawing also brings some problems with maintaining quality. Alternatively, this paper proposes a scheme for efficient and accurate update of digital map using point cloud data acquired by Terrestrial Laser Scanner (TLS). Initially, from the whole point cloud data, the building sides are extracted and projected onto a 2D image to trace out the 2D building footprints. In order to register the footprint extractions on the digital map, 2D Affine model is used. For Affine parameter estimation, the centroids of each footprint groups are randomly chosen and matched by means of a modified RANSAC algorithm. Based on proposed algorithm, the experimental results showed that it is possible to renew digital map using building footprint extracted from TLS data.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.19-28
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• 2006

Most conventional model updating methods must use mathematical objective function with experimental modal matrices and analytical system matrices or must use information about the gradient or higher derivatives of modal properties with respect to each updating parameter. Therefore, most conventional methods are not appropriate for complex structural system such as bridge structures due to stability problem in inverse analysis with ill-conditions. Sometimes, moreover, the updated model may have no physical meaning. In this paper, a new FE model updating method based on a hybrid optimization technique using genetic algorithm (GA) and Holder-Mead simplex method (NMS) is proposed. The performance of hybrid optimization technique on the nonlinear problem is demonstrated by the Goldstein-Price function with three local minima and one global minimum. The influence of the objective function is evaluated by the case study of a simulated 10-dof spring-mass model. Through simulated case studies, finally, the objective function is proposed to update mass as well as stiffness at the same time. And so, the proposed hybrid optimization technique is proved to be an efficient method for FE model updating.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.101-108
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• 2020

Recommender system is a must-have feature of e-commerce, since it provides customers with convenience in selecting products. Collaborative filtering is a widely-used and representative technique, where it gives recommendation lists of products preferred by other users or preferred by the current user in the past. Recently, researches on the recommendation system using deep learning artificial intelligence technologies are actively being conducted to achieve performance improvement. This study develops a collaborative filtering based recommender system using restricted Boltzmann machines of the deep learning technology by utilizing user ratings. Moreover, a learning parameter update algorithm is proposed for learning efficiency and performance. Performance evaluation of the proposed system is made through experimental analysis and comparison with conventional collaborative filtering methods. It is found that the proposed algorithm yields superior performance than the basic restricted Boltzmann machines.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.187-194
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• 2005

The background subtraction method is mainly used for the real-time extraction and tracking of moving objects from image sequences. In the outdoor environment, there are many changeable environment factors such as gradually changing illumination, swaying trees and suddenly moving objects , which are to be considered for an adaptive processing. Normally, GMM(Gaussian Mixture Model) is used to subtract the background by considering adaptively the various changes in the scenes, and the adaptive GMMs improving the real-time Performance were Proposed and worked. This paper, for on-line background subtraction, employed the improved adaptive GMM, which uses the small constant for learning rate a and is not able to speedily adapt the suddenly movement of objects, So, this paper Proposed and evaluated the dynamic control method of a using the adaptive selection of the number of component distributions and the global variances of pixel values.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.477-485
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• 2020

Finite element (FE) model based structural damage detection (SDD) methods play vital roles in effectively locating and quantifying structural damages. Among these methods, structural model updating should be conducted before SDD to obtain benchmark models of real structures. However, the characteristics of updating parameters are not reasonably considered in existing studies. Inspired by the l_∞ norm regularization, a novel anti-sparse representation method is proposed for structural model updating in this study. Based on sensitivity analysis, both frequencies and mode shapes are used to define an objective function at first. Then, by adding l_∞ norm penalty, an optimization problem is established for structural model updating. As a result, the optimization problem can be solved by the fast iterative shrinkage thresholding algorithm (FISTA). Moreover, comparative studies with classical regularization strategy, i.e. the l₂ norm regularization method, are conducted as well. To intuitively illustrate the effectiveness of the proposed method, a 2-DOF spring-mass model is taken as an example in numerical simulations. The updating results show that the proposed method has a good robustness to measurement noises. Finally, to further verify the applicability of the proposed method, a six-storey aluminum alloy frame is designed and fabricated in laboratory. The added mass on each storey is taken as updating parameter. The updating results provide a good agreement with the true values, which indicates that the proposed method can effectively update the model parameters with a high accuracy.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.1-12
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• 2004

As network systems are developed rapidly and network architectures are more complex than before, it needs to use PBNM(Policy-Based Network Management) in network system. Generally, architecture of the PBNM consists of two hierarchical layers: management layer and enforcement layer. A security policy server in the management layer should be able to generate new policy, delete, update the existing policy and decide the policy when security policy is requested. And the security policy server should be able to analyze and manage the alert messages received from Policy enforcement system in the enforcement layer for the available information. In this paper, we propose an alert analyzer using data mining. First, in the framework of the policy-based network security management, we design and implement an alert analyzes that analyzes alert data stored in DBMS. The alert analyzer is a helpful system to manage the fault users or hosts. Second, we implement a data mining system for analyzing alert data. The implemented mining system can support alert analyzer and the high level analyzer efficiently for the security policy management. Finally, the proposed system is evaluated with performance parameter, and is able to find out new alert sequences and similar alert patterns.

• The Journal of the Acoustical Society of Korea
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• v.32 no.5
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• pp.408-414
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• 2013

The famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems may become unstable in non-stationary noise environment. To solve this problem, Sun's algorithm and Akhtar's algorithm are developed based on modifying the reference signal in update of FxLMS algorithm, but these two algorithms have dissatisfactory stability in dealing with sustaining impulsive noise. In proposed algorithm, probability estimation and zero-crossing rate (ZCR) control are used to improve the stability and performance, at the same time, an optimal parameter selection based on fuzzy system is utilized. Computer simulation results prove the proposed algorithm has faster convergence and better stability in non-stationary noise environment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.222-228
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• 2010

In this paper, we propose a new approach to noise estimation incorporating spectral deviation with soft decision scheme to enhance the intelligibility of the degraded speech signal in non-stationary noisy environments. Since the conventional noise estimation technique based on soft decision scheme estimates and updates the noise power spectrum using a fixed smoothing parameter which was assumed in stationary noisy environments, it is difficult to obtain the robust estimates of noise power spectrum in non-stationary noisy environments that spectral characteristics of noise signal such as restaurant constantly change. In this paper, once we first classify the stationary noise and non-stationary noise environments based on the analysis of spectral deviation of noise signal, we adaptively estimate and update the noise power spectrum according to the classified noise types. The performances of the proposed algorithm are evaluated by ITU-T P. 862 perceptual evaluation of speech quality (PESQ) under various ambient noise environments and show better performances compared with the conventional method.