• Proceedings of the KSME Conference
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• 2001.11a
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• pp.192-197
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• 2001

To obtain $A_2$ experimentally in the $J-A_2$ theory, deformation field on the lateral surface of a CT specimen was to be determined using Laser speckle method. The crack growth was measured using direct current potential drop method and most procedure of experimental and data reduction was performed according to ASTM Standard E1737-96. Laser speckle images during crack propagation were monitored by two CCD cameras to cancel the effect of rotation and translation of the specimen. An algorithm to pursue displacement of a point from each image was developed and successfully used to measure $A_2$ continuously as the crack tip was propagated. The effects of specimen thickness on J-R curve and $A_2$ were explored.

• Progress in Superconductivity and Cryogenics
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• v.20 no.2
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• pp.29-33
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• 2018

Although high-temperature superconducting (HTS) magnets have the potential merit of producing ultra-high field (>25 T), they have been not easy to apply to Nuclear Magnetic Resonance (NMR) because of the difficulty of field homogeneity improvement. This paper presents a design technique of passive shimming for HTS magnets. Ferromagnetic shimming design code was developed though MALAB, which includes the optimization algorithm. The proper shim element size was determined by a simulation. This design technique was verified by a case study design of a 3-T HTS magnet. We succeed to improve field homogeneity of the magnet from 634 ppm to 6.39 ppm at 10-mm diameter sphere volume. Feasibility of passive shimming for all-HTS NMR magnet was confirmed by this result.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.13-13
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• 2010

Switching an elementary excitation by injecting a single carrier would offer the exciting opportunity for the ultra-high data storage technologies. However, there has been no methodology available to investigate the interaction of low energy discrete carriers with nano-structures. In order to map out the spatial dependency of such single carrier level interactions, we developed a pulse-and-probe algorithm, combining with low temperature scanning tunneling microscopy. The new tool, which we call single carrier spectroscopy, allows us to track the interaction with the target macrostructure with tunneling carriers on a single carrier basis. Using this tool, we demonstrate that it is possible not only to locally write and erase individual bi-solitons, reliably and reversibly, but also to track of creation yields of single and multiple bi-solitons. Bi-solitons are pairs of solitons that are elementary out-of-phase excitations on anti-ferromagnetically ordered pseudo-spin system of Si dimers on Si(001)-c(42) surfaces. We found that at low energy tunneling the single bisoliton creation mechanism is not correlated with the number of carriers tunneling, but with the production of a potential hole under the tip. An electric field at the surface determines the density of the local charge density under the tip, and band-bending. However a rapid, dynamic change of a field produces a potential hole that can be filled by energetic carriers, and the amount of energy released during filling process is responsible for the creation of bi-solitons. Our model based on the field-induced local hole gives excellent explanation for bi-soliton yield behaviors. Scanning tunneling spectroscopy data supports the existence of such a potential hole. The mechanism also explains the site-dependency of bi-soliton yields, which is highest at the trough, not on the dimer rows. Our study demonstrates that we can manipulate not just single atoms and molecules, but also single pseudo-spin excitations as well.

• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.237-246
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• 2011

Robots have been used in many fields due to its performance improvement and variety of its functionality, to the extent which robots can replace human tasks. Individual feature and better performance of robots are expected and required to be created. As their performances and functions have increased, systems have gotten more complicated. Multi mobile robots can perform complex tasks with simple robot system and algorithm. But multi mobile robots face much more complex driving problem than singular driving. To solve the problem, in this study, driving algorithm based on the energy method is applied to the individual robot in a group. This makes a cluster be in a formation automatically and suggests a cluster the automatic driving method so that they stably arrive at the target. The energy method mentioned above is applying attractive force and repulsive force to a special target, other robots or obstacles. This creates the potential energy, and the robot is controlled to drive in the direction of decreasing energy, which basically satisfies lyapunov function. Through this method, a cluster robot is able to create a formation and stably arrives at its target.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1921-1926
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• 2003

We build a local minimum free motion planning for mobile robots considering dynamic environments by simple sensor fusion assuming that there are unknown obstacles which can be detected only partially at a time by proximity sensors and can be cleaned up or moved slowly (dynamic environments). Potential field is used as a basic platform for the motion planning. To clear local minimum problem, the partial information on the obstacles should be memorized and integrated effectively. Sets of linked line segments (SLLS) are proposed as the integration method. Then robot's target point is replaced by virtual target considering the integrated sensing information. As for the main proximity sensors, we use laser slit emission and simple web camera since the system gives more continuous data information. Also, we use ultrasonic sensors as the auxiliary sensors for simple sensor fusion considering the advantages in that they give exact information about the presence of any obstacle within certain range. By using this sensor fusion, the dynamic environments can be dealt easily. The performance of our algorithm is validated via simulations and experiments.

• Journal of the Korean Geotechnical Society
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• v.28 no.8
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• pp.55-63
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• 2012

The evaluation of shear modulus (or shear wave velocity) profile of the site is very important in various fields of geotechnical engineering and various surface wave methods have applied to determine the shear wave velocity profiles and showed good performance. Surface wave methods evaluate the dispersion curve in the field and determine the shear wave velocity profile through the inversion process. In this paper, the automated inversion process using the genetic algorithm is developed for HWAW method which is one of surface wave methods recently developed. The proposed method uses the error function based on the wavelength domain dispersion curve and can determine the reliable shear wave velocity profile not only in shallow depth but also in deep depth. To estimate the validity of the proposed method, numerical simulations and field test were performed and the proposed method was applied to determine the shear wave velocity profiles. Through the numerical simulations and field applications, the promising potential of the proposed method was verified.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.127-130
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• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• Advances in robotics research
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• v.2 no.3
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• pp.219-236
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• 2018

This research focuses on controlling robots and their formations using rough mereology as a means for spatial reasoning. The authors present the state of the art theory behind path planning, robot cooperation domains and ways of creating robot formations. Furthermore, the theory behind Rough Mereology as a way of implementing mereological potential field based path creation and navigation for single and multiple robots is described. An implementation of the algorithm is shown in simulation using RoboSim simulator. Five formations are tested (Line, Rhomboid, Snake, Circle, Cross) along with three decision systems (First In, Leader First, Horde Mode) as compared to other methods.

• Journal of the Semiconductor & Display Technology
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• v.8 no.4
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• pp.43-48
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• 2009

The aim of this paper is to describe the development of the electron-beam optic analysis algorithm for simulating the e-beam behavior concerned with electrostatic lenses and their focal properties in the micro-column of the multi-beam lithography system. The electrostatic lens consists of an array of electrodes held at different potentials. The electrostatic lens, the so-called einzel lens, which is composed of three electrodes, is used to focus the electron beam by adjusting the voltages of the electrodes. The optics of an electron beam penetrating a region of an electric field is similar to the situation in light optics. The electron is accelerated or decelerated, and the trajectory depends on the angle of incidence with respect to the equi-potential surfaces of the field. The performance parameters, such as the working distances and the beam diameters are obtained by the computational simulations as a function of the focusing voltages of the einzel lens electrodes. Based on the developed simulation algorithm, the high performance of the micro-column can be achieved through optimized control of the einzel lens.