• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.49-55
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• 2017

Most motor cases adopt deep drawing products, which are excellent in waterproof functions, concentricity, right angle, and quality. In addition, the blower motor and seat motor, which are installed in the car interior and do not require waterproof function, adopts a multi-forming manufacturing method. The deep drawing process requires an expensive transfer press that can digest approximately 12 processes, such as drawing, trimming and piercing. On the other hand, products can be produced with low investment because the multi-forming method is composed of one multi-forming machine or one multi-forming machine and one press. The multi-forming machine is a high-priced facility that is mostly imported and a bending / shearing process multi-foaming machine, which was developed by domestic small and medium-sized enterprises, is not enough to reduce the production cost. An integral multi - forming machine is used as a limited working method for thin material and small products. A large product and thick material has a high shear load. A large product and thick material has a high shear load and uses a single crank press. After blanking, the worker manually feeds the material to a multi-forming machine. When the bending operation is performed in the multi-forming machine, it is transferred to the press again to calibrate the dimensions. This variance in work processes has resulted in lower cost competitiveness due to the lower productivity, quality issues, and excessive operator input. The aim of this study was to establish a stable and cost - effective production system through bending / shearing process separation and facility automation.

• Current Optics and Photonics
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• v.4 no.1
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• pp.69-80
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• 2020

A Rayleigh Lidar used for wind detection works by transmitting laser pulses to the atmosphere and receiving backscattering signals from molecules. Because of the weak backscattering signals, a lidar usually uses a high sensitivity photomultiplier as detector and photon counting technology for signal collection. The capturing of returned extremely weak backscattering signals requires the lidar to work on dark background with a long time accumulation to get high signal-to-noise ratio (SNR). Because of the strong solar background during the day, the SNR of lidar during daytime is much lower than that during nighttime, the altitude and accuracy of detection are also restricted greatly. Therefore this article describes an ultra-narrow bandwidth filter (UNBF) that has been developed on 354.7 nm wavelength of laser. The UNBF is used for suppressing the strong solar background that degrades the performance of Rayleigh wind lidar during daytime. The optical structure of UNBF consists of an interference filter (IF), a low resolution Fabry-Perot interferometer (FPI) and a high resolution FPI. The parameters of each optical component of the UNBF are presented in this article. The transmission curve of the aligned UNBF is measured with a tunable laser. Contrasting the result of with-UNBF and with-IF shows that the solar background received by a Licel transient recorder decreases by 50~100 times and that the SNR with-UNBF was improved by 3 times in the altitude range (35 km to 40 km) compared to with-IF at 10:26 to 10:38 on August 29, 2018. By the SNR comparison at four different times of one day, the ratio-values are larger than 1 over the altitude range (25~50 km) in general, the results illustrate that the SNR with-UNBF is better than that with-IF for Rayleigh Lidar during daytime and they demonstrate the effective improvements of solar background restriction of UNBF.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.157-164
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• 2005

Urban conditions such as underground facilities and ambient noises due to cultural activity restrict the application of conventional geophysical techniques in general. We used the refraction microtremor (REMI) technique as an alternative way to get the geotechnical information, in particular shear-wave (S-wave) velocity information, at a site along an existing rail road. The REMI method uses ambient noises recorded using standard refraction equipment to derived shear-wave velocity information at a site. It does a wavefield transformation on the recorded wavefield to produce Rayleigh wave dispersion curve, which are then picked and modeled to get the shear-wave velocity structure. At this site the vibrations from the running trains provided strong noise sources that allowed REMI to be very effective. REMI was performed along the planned new underground rail tunnel. In addition, Suspension PS logging (SPS) were carried out at selected boreholes along the profile in order to draw out the quantitative relation between the shear wave velocity from the PS logging and the rock mass rating (RMR) determined from the inspection of the cores recovered from the same boreholes, These correlations were then used to relate the shear-wave velocity derived from REMI to RMR along the entire profile. The correlation between shear wave velocity and RMR was very good and so it was possible to estimate the RMR of the total zone of interest for the design of underground tunnel,

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.108-113
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• 2006

Urban conditions, such as existing underground facilities and ambient noise due to cultural activity, restrict the general application of conventional geophysical techniques. At a tunnelling site in an urban area along an existing railroad, we used the refraction microtremor (REMI) technique (Louie, 2001) as an alternative way to get geotechnical information. The REMI method uses ambient noise recorded by standard refraction equipment and a linear geophone array to derive a shear-wave velocity profile. In the inversion procedure, the Rayleigh wave dispersion curve is picked from a wavefield transformation, and iteratively modelled to get the S-wave velocity structure. The REMI survey was carried out along the line of the planned railway tunnel. At this site vibrations from trains and cars provided strong seismic sources that allowed REMI to be very effective. The objective of the survey was to evaluate the rock mass rating (RMR), using shear-wave velocity information from REMI. First, the relation between uniaxial compressive strength, which is a component of the RMR, and shear-wave velocity from laboratory tests was studied to learn whether shear-wave velocity and RMR are closely related. Then Suspension PS (SPS) logging was performed in selected boreholes along the profile, in order to draw out the quantitative relation between the shear-wave velocity from SPS logging and the RMR determined from inspection of core from the same boreholes. In these tests, shear-wave velocity showed fairly good correlation with RMR. A good relation between shear-wave velocity from REMI and RMR could be obtained, so it is possible to estimate the RMR of the entire profile for use in design of the underground tunnel.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.15-22
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• 2002

Image segmentation methods partition an image into meaningful regions. For image composition and analysis, it is desirable for the partitioned regions to represent meaningful objects in terms of human perception and manipulation. Despite the recent progress in image understanding, however, most of the segmentation methods mainly employ low-level image features and it is still highly challenging to automatically segment an image based on high-level meaning suitable for human interpretation. The concept of HCI (Human Computer Interaction) can be applied to operator-assisted image segmentation in a manner that a human operator provides guidance to automatic image processing by interactively supplying critical information about object boundaries. Intelligent Scissors and Snakes have demonstrated the effectiveness of human-assisted segmentation [2] [1]. This paper presents a method for interactive image segmentation for more efficient and effective detection and tracking of object boundaries. The presented method is partly based on the concept of Intelligent Scissors, but employs the well-established Canny edge detector for stable edge detection. It also uses "sewing method" for including weak edges in object boundaries, and 5-direction search to promote more efficient and stable linking of neighboring edges than the previous methods.

• Journal of the Korea Computer Graphics Society
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• v.15 no.3
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• pp.1-9
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• 2009

Traditional computer simulation uses only traditional input and output devices. With the recent emergence of haptic techniques, which can give users kinetic and tactile feedback, the field of computer simulation is diversifying. In particular, as the virtual-reality-based surgical simulation has been recognized as an effective training tool in medical education, the practical virtual simulation of surgery becomes a stimulating new research area. The surgical simulation framework should represent the realistic properties of human organ for the high immersion of a user interaction with a virtual object. The framework should make proper both haptic and visual feedback for high immersed virtual environment. However, one model may not be suitable to simulate both haptic and visual feedback because the perceptive channels of two feedbacks are different from each other and the system requirements are also different. Therefore, we separated two models to simulate haptic and visual feedback independently but at the same time. We propose an adaptive mass-spring method as a multi-modal simulation technique to synchronize those two separated models and present a framework for a dual model of simulation that can realistically simulate the behavior of the soft, pliable human body, along with haptic feedback from the user's interaction.

• Journal of Dental Anesthesia and Pain Medicine
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• v.20 no.4
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• pp.195-202
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• 2020

Background: Nasotracheal intubation is the most commonly used method to secure the field of view when performing surgery on the oral cavity or neck. Like orotracheal intubation, nasotracheal intubation uses a laryngoscope. Hemodynamic change occurs due to the stimulation of the sympathetic nervous system. Recently, video laryngoscope with a camera attached to the end of the direct laryngoscope blade has been used to minimize this change. In this study, we investigated the optimal effect-site concentration (Ce) of remifentanil for minimizing hemodynamic responses during nasotracheal intubation with a video laryngoscope. Methods: Twenty-one patients, aged between 19 and 60 years old, scheduled for elective surgery were included in this study. Anesthesia was induced by slowly injecting propofol. At the same time, remifentanil infusion was initiated at 3.0 ng/ml via target-controlled infusion (TCI). When remifentanil attained the preset Ce, nasotracheal intubation was performed using a video laryngoscope. The patient's blood pressure and heart rate were checked pre-induction, right before and after intubation, and 1 min after intubation. Hemodynamic stability was defined as an increase in systolic blood pressure and heart rate by 20% before and after nasotracheal intubation. The response of each patient determined the Ce of remifentanil for the next patient at an interval of 0.3 ng/ml. Results: The Ce of remifentanil administered ranged from 2.4 to 3.6 ng/ml for the patients evaluated. The estimated optimal effective effect-site concentrations of remifentanil were 3.22 and 4.25 ng/ml, that were associated with a 50% and 95% probability of maintaining hemodynamic stability, respectively. Conclusion: Nasotracheal intubation using a video laryngoscope can be successfully performed in a hemodynamically stable state by using the optimal remifentanil effect-site concentration (Ce₅₀, 3.22 ng/ml; Ce₉₅, 4.25 ng/ml).

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.221-228
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• 2013

It is impossible directly to find a prime number p,q of a large semiprime n = pq using Trial Division method. So the most of the factorization algorithms use the indirection method which finds a prime number of p = GCD(a-b, n), q=GCD(a+b, n); get with a congruence of squares of $a^2{\equiv}b^2$ (mod n). It is just known the fact which the area that selects p and q about n=pq is between $10{\cdots}00$ < p < $\sqrt{n}$ and $\sqrt{n}$ < q < $99{\cdots}9$ based on $\sqrt{n}$ in the range, [$10{\cdots}01$, $99{\cdots}9$] of $l(p)=l(q)=l(\sqrt{n})=0.5l(n)$. This paper proposes the method that reduces the range of p using information obtained from n. The proposed method uses the method that sets to $p_{min}=n_{LR}$, $q_{min}=n_{RL}$; divide into $n=n_{LR}+n_{RL}$, $l(n_{LR})=l(n_{RL})=l(\sqrt{n})$. The proposed method is more effective from minimum 17.79% to maxmimum 90.17% than the method that reduces using $\sqrt{n}$ information.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.