• Geophysics and Geophysical Exploration
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• v.4 no.4
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• pp.133-144
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• 2001

An effective seismic reflection technique for mapping the cavities and bedrock surface in carbonate rocks is described. The high resolution seismic reflection images were successfully registered by using the hydrophones employed in the stream-water driven trench, and were effectively focused by applying optimal data processing sequences. The strategy included enhancement of the signal interfered with the large-amplitude scattering noise, through pre- and post stack processing such as time-variant filtering, bad-trace editing, residual statics, velocity analysis, and careful muting after NMO (normal moveout) correction. The major reflections including the bedrock surface were mapped with the desired resolution and were correlated to the seismic crosshole tomographic data. Shallow major reflectors could be identified and analyzed on the AGC (auto gain control)-applied field records. Three subhorizontal layers were identified with their distinct velocities; overburden (<3000 m/s), sediments (3000-4000 m/s), limestone bedrock (>4000 m/s). Taking into account of no diffraction effects in the field records, gravel-rich overburdens and sediments are considered to be well sorted. Based on the images mapped consistently on the whole survey line and seismic velocity increasing with depth, this area probably lacks in sizable cavities (if any, no air-filled cavities).

• Journal of Energy Engineering
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• v.19 no.1
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• pp.8-15
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• 2010

As environmental friendly energy system, distributed micro gasturbine is focused on new energy source for overcoming brand new construction area of power generation. This distributed micro gasturbine system has the powerful characteristics as belows; 1) environmental friendly features NOx < 9 ppm, noise < 65 db 2) various fuel flexbility which is used such as natural gas, diesel, low calory new & renewable fuel, kerosene. 3) high specific output power based on small area and is avilable for very easy and compact installation. There are many new installation sites in USA and Japan from 1998. On the other hand the exhisting large power system was constructued by the sea side, this compact power system is now installed by enduser in downtown area and supplying combined heat & power, has the various apllication on-site power generation. In recently, there is the very important issue for new & reliablbe energy development and spreading out. This paper represent as belows for important system characteristics; 1) grid connection modeling 2) system operation characteristics 3) on-site operation result and evaluation output of power quality analysis.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.237.1-237.1
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• 2014

Etching is one of the most important steps in semiconductor manufacturing. In etch process control a critical task is to stop the etch process when the layer to be etched has been removed. If the etch process is allowed to continue beyond this time, the material gets over-etched and the lower layer is partially removed. On the other hand if the etch process is stopped too early, part of the layer to be etched still remains, called under-etched. Endpoint detection (EPD) is used to detect the most accurate time to stop the etch process in order to avoid over or under etch. The goal of this research is to develop a hardware and software system for EPD. The hardware consists of an Optical Plasma Monitor (OPM) sensor which is used to continuously monitor the plasma optical emission intensity during the etch process. The OPM software was developed to acquire and analyze the data to perform EPD. Our EPD algorithm is based on the following theory. As the etch process starts the plasma generated in the vacuum is added with the by-products from the etch reactions on the layer being etched. As the endpoint reaches and the layer gets completely removed the plasma constituents change gradually changing the optical intensity of the plasma. Although the change in optical intensity is not apparent, the difference in the plasma constituents when the endpoint has reached leaves a unique signature in the data gathered. Though not detectable in time domain, this signature could be obscured in the frequency spectrum of the data. By filtering and analysis of the changes in the frequency spectrum before and after the endpoint we could extract this signature. In order to do that, first, the EPD algorithm converts the time series signal into frequency domain. Next the noise in the frequency spectrum is removed to look for the useful frequency constituents of the data. Once these useful frequencies have been selected, they are monitored continuously in time and using a sub-algorithm the endpoint is detected when significant changes are observed in those signals. The experiment consisted of three kinds of etch processes; ashing, SiO2 on Si etch and metal on Si etch to develop and evaluate the EPD system.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.40-48
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• 2009

Leading of the computer, IT technology has make great strides. As a information-industry-community was highly developed, user's needs to convenience about intelligence and humanization of interface is being increase today. Nowadays, researches with are related to BCI are progress put the application-technology development first in importance eliminating research about fountainhead technology with DB construction. These problems are due to a BCI-related research studies have not overcome the initial level, and not toward a systematic study. Brain wave are collected from subjects is a signal that the signal is appropriate and necessary in the experiment is difficult to distinguish. In addition, brain wave that it's not necessary to collect the experiment, serious eyes flicker, facial and body movements of an EMG and electrodes attached to the state, noise, vibration, etc. It is hard to collect accurate brain wave was caused by mixing disturbance wave in experiment on the environment. This movement, and the experiment of subject impact on the environment due to the mixing disturbance wave can cause that lowering cognitive and decline of efficiency when embodied BCI system. Therefore, in this paper, we propose an accurate and efficient brain-wave DB building system that more exactness and cognitive basis studies when embodied BCI system with brain-wave. For the minimize about brain wave DB with mixing disturbance, we propose a DB building method using an automatic control and prevent unnecessary action, put to use the subjects face tracking.

• Design & Manufacturing
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• v.12 no.2
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• pp.40-45
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• 2018

Recently, in construction equipment machinery production, development has focused on environmentally-friendly functions to improve existing production capacity. For excavators as well, emphasis has been placed on response to environmental regulations, miniaturization, and noise reduction, while technology is being developed considering cost reduction and safety.Accordingly, the front support, an inner reinforcement part of the excavator, as well as high-strength steel plates to improve safety and reduce weight, are being applied.However, in the case of high-strength materials, Springback occurs in the final formed part due to high residual stress during product forming. Derivation of a forming or product shaping process to reduce springback is needed. Accordingly, regarding the front support, an inner reinforcement part of the excavator, this study derived a method to improve springback and secure shape stiffness through analysis of the springback occurrence rate and springback causes through a forming analysis.As for the results of analyzing the springback occurrence rate of existing products through forming analysis, springback of -22.6 mm < z < 27.35 mm occurred on the z-axis, and it was confirmed that springback occurred due to the stiffness reinforcing bead of the upper and middle parts of the product.To control product residual stress and springback, we confirmed a tendency of springback reduction through local pre-cutting and stiffness reinforcement bead relocation.In the local pre-cutting model, springback was slightly reduced by 5.3% compared with the existing model, an insignificant reduction effect. In the stiffness reinforcement bead relocation model, when an X-shaped stiffness reinforcement bead was added to each corner portion of the product, springback was reduced by at least 80%.The X-shaped bead addition model was selected as the springback reduction model, and the level of stiffness compared to the existing model was confirmed through a structural analysis.The X-shaped bead additional model showed a stress springback of 90% and springback reduction of 7.4% compared with the existing model, indicating that springback and stiffness will be reinforced.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.26 no.2
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• pp.178-187
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• 2016

Objectives: Diisocyanates are a potent inducer of diseases of the airways, especially asthma. In this study, toluenediamine(TDA) and methylenedianiline(MDA) in urine were evaluated as biomarkers of exposure to tolunenediisocyanate(TDI) and methylenediphenyl diisocyanate(MDI), respectively. Methods: Workers exposed to TDI and MDI, as well as non-occupationally exposed subjects, were studied and pre- and post-shift urine samples were collected from 8 control subjects and 8 workers from a factory which manufactures polyurethane products for reducing noise and vibration in automobiles. Airborne TDI and MDI(n=8) were sampled on solvent-free glass filters impregnated with n-butylamine and detected by liquid chromatography atmospheric pressure ionization tandem mass spectrometry. Urinary TDA and MDA were detected as pentafluoropropionic acid anhydride(PFPA) derivatives by liquid chromatography electrospray ionization tandem mass spectrometry. Results: The median levels of urinary 2,6-TDA(p<0.001), 2,4-TDA(p=0.001), and MDA(p<0.001) of workers in post-shift samples were significantly higher than those of controls. The median levels of urinary 2,6-0TDA($0.63{\mu}g/g$ creatinine vs $0.34{\mu}g/g$ creatinine, p=0.017) and MDA($4.21{\mu}g/g$ creatinine vs $3.18{\mu}g/g$ creatinine, p=0.017) of workers in post-shift samples were significantly higher than those of the pre-shift samples. There were significant correlations between the urinary 2,6-TDA, 2,4-TDA, and MDA of workers in post-shift samples and the airborne 2,6-TDI(rho=0.952, p<0.001), 2,4-TDI(rho=0.833, p=0.001), and MDI(rho=0.952, p<0.001). Conclusions: These urinary diamines, metabolites of diisocyanates, in post-shift samples were useful biomarkers to assess occupational exposure to diisocyanates.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

• Analytical Science and Technology
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• v.18 no.3
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• pp.250-256
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• 2005

A selective method of high performance liquid chromatography with UV detector has been applied to determine 4 tetracycline antibiotics in the animal food, simultaneously. The targets were chlortetracycline (CTC), doxycycline (DC), oxytetracycline (OTC), and tetracycline (TC) that are used routinely in veterinary medicine for prevention and control of disease. Food samples were beef, pork, chicken, milk, whole egg, flatfish (Limanda yokohamae), jacopever (Sebastes hubbsi), seabream (Chrysophrys major), eel (Anguilla japonica) and lobster (Hommarus americanus). After extracting food samples with 20% trichloroacetic acid and McIlvaine buffer, they were purified by a $C_18$ SPE cartridge with 0.01M methanolic oxalic acid solution. The concentrated residue was re-dissolved in methanol, filtered, cleaned up and analyzed on a $C_18$ column. The mobile phase was a mixture of 0.01M oxalic acid and acetonitrile with a gradient ratio from 85:15 to 60:40. The UV wavelength was 365 nm. The overall recoveries were ranged from 71% to 98% and the limit of detections were 0.022 for CTC, 0.012 for DC and OTC and 0.009 mg/kg for TC at signal/noise > 3, respectively. As results, CTC, DC and TC were not detected in all selected food samples, however, OTC was detected in meat and fishes. The determined level of OTC was 0.04 ppm for pork, 0.17 ppm for flatfish and 0.05 and 0.08 ppm for jacopever, that were within the Maximum Residue Limits (MRLs) in the food.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.1-7
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• 2020

Deep neural networks(DNN), which are used as approximation functions in reinforcement learning (RN), theoretically can be attributed to realistic results. In empirical benchmark works, time difference learning (TD) shows better results than Monte-Carlo learning (MC). However, among some previous works show that MC is better than TD when the reward is very rare or delayed. Also, another recent research shows when the information observed by the agent from the environment is partial on complex control works, it indicates that the MC prediction is superior to the TD-based methods. Most of these environments can be regarded as 5-step Q-learning or 20-step Q-learning, where the experiment continues without long roll-outs for alleviating reduce performance degradation. In other words, for networks with a noise, a representative network that is regardless of the controlled roll-outs, it is better to learn MC, which is robust to noisy rewards than TD, or almost identical to MC. These studies provide a break with that TD is better than MC. These recent research results show that the way combining MC and TD is better than the theoretical one. Therefore, in this study, based on the results shown in previous studies, we attempt to exploit a random balance with a mixture of TD and MC in RL without any complicated formulas by rewards used in those studies do. Compared to the DQN using the MC and TD random mixture and the well-known DQN using only the TD-based learning, we demonstrate that a well-performed TD learning are also granted special favor of the mixture of TD and MC through an experiments in OpenAI Gym.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.