• Wind and Structures
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• v.7 no.6
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• pp.405-420
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• 2004

In common approaches, bridge dynamics under wind action is analyzed by modeling the interaction between fluid and structure by means of transient wind loads acting over the structure itself. Amid various possible manners to describe such types of loads, a representation based on families of 'indicial functions' is adopted here. The aim is to investigate its flexibility to capture the main features of wind-bridge interaction. A set of coefficients is involved in indicial functions. The values that one may attribute to them suffer uncertainties coming from experimental errors affecting data. Here, the sensitivity of a 2-DOF schematic model to the variations of these coefficients is investigated at fixed values of dynamic derivatives and for various types of indicial functions. It is shown how parameter variations influence phase portraits.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.10
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• pp.396-401
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• 1984

This paper present a method of using simplified models for deriving suboptimal controllers to the original higher-order systems. Routh approximation method is a very useful technique for reducing the order of a linear systems. This method dose not require a knowlege of system eigenvalues and eigenvectors and possesses many desirable features such as preservation of reduced order model stability and minimum computational requirements. These properties are utilized to derive suboptimal controllers in this paper. In order to implement htese ocntrollers on the original system, the relationship between the state vectors of the original system and the reduced order models is required. A procedure fir evaluating an approximate aggregation matrix is also developed. A numerical example is given for the illustration of this method, shich is compared with the existing Model aggregation method in the resultant figures.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1370-1378
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• 2018

There is a problem to measure neutral bio-signals during sleep because of inconvenience of attaching lots of sensors. In this study, we measured single electrocardiogram(ECG) signal and analyzed the correlation with sleep. After R-peak detection from ECG signal, we extracted 9 features from time and frequency domain of heart rate variability(HRV). Mean of HRV, RR intervals differing more than 50ms(NN50), and divided by the total number of all RR intervals(pNN50) have significant differences in each sleep stage. Specially, the mean HRV has an average of 87.8% accuracy in classifying sleep and awake status. In the future, the measurement ECG signal minimizes inconvenience of attaching sensors during sleep. Also, it can be substituted for the standard sleep measurement method.

• Smart Structures and Systems
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• v.29 no.6
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• pp.767-775
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• 2022

Metal additive manufacturing (AM), also known as metal three-dimensional (3D) printing, produces 3D metal products by repeatedly adding and solidifying metal materials layer by layer. During the metal AM process, products experience repeated local melting and cooling using a laser or electron beam, resulting in product defects, such as warpage, cracks, and internal pores. Such defects adversely affect the final product. This paper proposes the in situ monitoring-based warpage prediction of metal AM products with experimental feature extraction. The temperature profile of the metal AM substrate during the process was experimentally collected. Time-domain features were extracted from the temperature profile, and their relationships to the warpage mechanism were investigated. The standard deviation showed a significant linear correlation with warpage. The findings from this study are expected to contribute to optimizing process parameters for metal AM warpage reduction.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.187-195
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• 2024

Light detection and ranging (LiDAR), a widely used sensor in mobile robots and autonomous vehicles, has its most important function as measuring the range of objects in three-dimensional space and generating point clouds. These point clouds consist of the coordinates of each reflection point and can be used for various tasks, such as obstacle detection and environment recognition. However, several processing steps are required, such as three-dimensional modeling, mesh generation, and rendering. Efficient data processing is crucial because LiDAR provides a large number of real-time measurements with high sampling frequencies. Despite the rapid development of controller computational power, simplifying the computational algorithm is still necessary. This paper presents a method for estimating the presence of curbs, humps, and ground tilt using range measurements from a single horizontal or vertical scan instead of point clouds. These features can be obtained by data segmentation based on linearization. The effectiveness of the proposed algorithm was verified by experiments in various environments.

• Journal of the Korean Society of Safety
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• v.21 no.2 s.74
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• pp.143-149
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• 2006

This paper deals with the space-time finite element analysis of two-dimensional vibration problem with a single variable. The method of space-time finite elements enables the simpler solution than the usual finite element analysis with discretization in space only. We present a discretization technique in which finite element approximations are used in time and space simultaneously for a relatively large time period. The weighted residual process is used to formulate a finite element method for a space-time domain. A stability problem is described and some investigations for chosen type of rectangular space-time finite elements are carried out. Instability is caused by a too large time step of successive time steps in the traditional time-dependent problems. It has been shown that the numerical stability of time-stepping on the larger time steps is quite good. The unstructured space-time finite element not only overcomes the shortcomings of the stability in the traditional numerical methods, but it is also endowed with the features of an effective computational technique. Some numerical examples have been presented to illustrate the efficiency of the described method.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.184-189
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• 2004

A soil moisture measuring method for a hillslope of Korean watershed was developed to configure spatial-temporal distribution of soil moisture. Intensive surveying of topography had been performed to make a refined digital elevation model(DEM) and the hydrological interpretation from flow distribution algorithm was incorporated through reverse surveying. Moreover, A long term measurement system was established to maximize representative features of spatial variation of soil moisture and operated from October 19 to 21, 2003. TDR(Time Domain Reflectometry) with a multiplex monitoring system has been operated for accurate measurements. Measurements were performed at the right side hillslope of Buprunsa located at the sulmachun watershed. The data of temporal and spatial soil moisture variation by rainfall event were collected and the variations of soil moisture were well captured.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.

• Journal of Genetic Medicine
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• v.16 no.1
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• pp.39-42
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• 2019

KBG syndrome is an autosomal dominant syndrome presenting with macrodontia, distinctive facial features, skeletal anomalies, and neurological problems caused by mutations in the ankyrin repeat domain 11 (ANKRD11) gene. The diagnosis of KBG is difficult in very young infants as the characteristic macrodontia and typical facial features are not obvious. The youngest patient diagnosed to date was almost one year of age. We here describe a 2-month-old Korean boy with distinctive craniofacial features but without any evidence of macrodontia due to his very early age. He also had a congenital megacolon without ganglion cells in the rectum. A de novo deletion of exons 5-9 of the ANKRD11 gene was identified in this patient by exome sequencing and real-time genomic polymerase chain reaction. As ANKRD11 is involved in the development of myenteric plexus, a bowel movement disorder including a congenital megacolon is not surprising in a patient with KBG syndrome and has possibly been overlooked in past cases.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.518-528
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• 2014

In this paper, we propose a method to recognize the action direction of human by developing 4D space-time (4D-ST, [x,y,z,t]) features. For this, we propose 4D space-time interest points (4D-STIPs, [x,y,z,t]) which are extracted using 3D space (3D-S, [x,y,z]) volumes reconstructed from images of a finite number of different views. Since the proposed features are constructed using volumetric information, the features for arbitrary 2D space (2D-S, [x,y]) viewpoint can be generated by projecting the 3D-S volumes and 4D-STIPs on corresponding image planes in training step. We can recognize the directions of actors in the test video since our training sets, which are projections of 3D-S volumes and 4D-STIPs to various image planes, contain the direction information. The process for recognizing action direction is divided into two steps, firstly we recognize the class of actions and then recognize the action direction using direction information. For the action and direction of action recognition, with the projected 3D-S volumes and 4D-STIPs we construct motion history images (MHIs) and non-motion history images (NMHIs) which encode the moving and non-moving parts of an action respectively. For the action recognition, features are trained by support vector data description (SVDD) according to the action class and recognized by support vector domain density description (SVDDD). For the action direction recognition after recognizing actions, each actions are trained using SVDD according to the direction class and then recognized by SVDDD. In experiments, we train the models using 3D-S volumes from INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset and recognize action direction by constructing a new SNU dataset made for evaluating the action direction recognition.