• Korean Journal of Materials Research
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• v.21 no.1
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• pp.1-7
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• 2011

Methods of producing hydrogen include steam reforming, electrochemical decomposition of water, and the SI process. Among these methods, the Sulfur iodine process is one of the most promising processes for hydrogen production. The thermochemical sulfur-iodine (SI) process uses heat from a high-temperature-gas nuclear reactor to produce $H_2$ gas; this process is known for its production of clean energy as it does not emit $CO_2$ from water. But the SI-process takes place in an extremely corrosive environment for the materials. To endure SI environments, the materials for the SI environment will have to have strong corrosion resistance. This work studies the corrosion resistances of the Fe-Si, Ni-Ti and Ni Alloys, which are tested in SI-process environments. Among the SI-process environments, the conditions of boiling sulfuric acid and decomposed sulfuric acid are selected in this study. Before testing in boiling sulfuric acid environments, the specimens of Fe-4.5Si, Fe-6Si, Ni-4.5Si, Ni-Ti-Si-Nb and Ni-Ti-Si-Nb-B are previously given heat treatment at $1000^{\circ}C$ for 48 hrs. The reason for this heat treatment is that those specimens have a passive film on the surface. The specimens are immersed for 3~14 days in 98wt% boiling sulfuric acid. Corrosion rates are measured by using the weight change after immersion. The corrosion rates of the Fe-6Si and Ni-Ti-Si-Nb-B are found to decrease as the time passes. The corrosion rates of Fe-6si and Ni-Ti-Si-Nb-B are measured at 0.056 mm/yr and 0.16 mm/yr, respectively. Hastelloy-X, Alloy 617, Alloy 800H and Haynes 230 are tested in the decomposed sulfuric acid for one day. Alloy 800H was found to show the best corrosion resistance among the materials. The corrosion rate of Alloy 800H is measured at -0.35 mm/yr. In these results, the corrosion resistance of materials depends on the stability of the oxide film formed on the surface. After testing in boiling sulfuric acid and in decomposed sulfuric acid environments, the surfaces and compositions of specimens are analyzed by SEM and EDX.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.5
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• pp.768-774
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• 2008

In many areas of speech signal processing such as automatic speech recognition and packet based voice communication technique, VAD (voice activity detection) plays an important role in the performance of the overall system. In this paper, we present a new feature parameter for VAD which is the product of energy of the signal and the difference of two types of entropies. For this end, we first define a Mel filter-bank based entropy and calculate its difference from the conventional entropy in frequency domain. The difference is then multiplied by the spectral energy of the signal to yield the final feature parameter which we call PEED (product of energy and entropy difference). Through experiments. we could verify that the proposed VAD parameter is more efficient than the conventional spectral entropy based parameter in various SNRs and noisy environments.

• Journal of the Korean Solar Energy Society
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• v.38 no.6
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• pp.65-72
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• 2018

Since data-driven building technologies have been widely applied to building energy systems, the accuracy of building sensors has more impacts on the building performance and system performance analysis. Various building sensors, however, can have typical errors including a random error (noise) and a systematic error (bias). The systematic error is indicated by the difference between the mean of measurements and their true value. It may occur due to the sensor's physical condition, measured phenomena, working environments inside the systems. Unfortunately, a conventional calibration method has limitations in calibrating the systematic errors because of the difference between working environments and calibration conditions. In such situations, a novel sensor calibration method is needed to handle various sensor errors, especially for systematic errors, in building energy systems having various thermodynamic environments. This study proposes a building sensor calibration method named Virtual In-situ Calibration (VIC) and shows how it is applied into a real building system and how it solves the sensor errors.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2017.05a
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• pp.209-210
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• 2017

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.519-526
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• 2022

Electronic personal dosimeter (EPD) provide real time monitoring and a direct indication of the accumulated dose or dose rate in terms of personal dose. Most EPD do not perform well in low energy photon radiation fields present in medical radiation environments. It has poor responsibility and large error rate for low energy photon radiation of medical radiation environments. This study evaluated to optimal additional filtration for EPD using silicon PIN photodiode detector form 40 to 120 kVp range in medical radiation environments. From 40 to 80 kVp energy range, Al 0.2 mm and Sn 1.0 mm overlapped filtration showed good responsibility to dose rate and from 80 kVp to 120 kVp energy range, Al 0.2 mm and Sn 1.6 mm overlapped filtration showed good responsibility to dose rate.

• Journal of Sensor Science and Technology
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• v.23 no.3
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• pp.173-177
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• 2014

A Rhodamine-based fiber-optic sensor has been developed to detect mercury ions in aqueous environments. The fiber-optic sensor was composed of a mercury-sensing thin film, plastic optical fibers, and a spectrometer. The mercury-sensing thin film with the synthesized Rhodamine derivatives was fabricated with Sol-Gel process. A light emitted by a light source is guided by plastic optical fibers into the thin film in an aqueous solution and a reflected light is analyzed with the spectrometer. The experiment exhibits that an absorbance in the thin film is increased as mercury concentration is increased in the solution and the absorbance by mercury is higher than that by other heavy metals. The fiber-optic sensor exhibits high chromogenic phenomenon of mercury ions among various heavy metals and the correlation between absorbance and mercury concentration in the aqueous environments.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.65.3-65.3
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• 2017

With the advent of high energy/power lasers, extreme conditions, such as those found in astrophysical environments, can be reproduced in laboratory. The scaling between laboratory and astrophysical environments, especially for viscosity and resistivity that govern dissipation processes, is not perfect. Yet, the similarity is close enough to make laboratory experiments relevant for astrophysics. The results have been encouraging, in the sense of suggesting the possibility of exploring fundamental physical processes at play in astrophysical phenomena. In this talk, I will review a few successfully performed and ongoing experiments, such as those for turbulence and magnetic field generation in fluid regime and collisionless shock wave in plasma regime.

• Journal of Korea Multimedia Society
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• v.11 no.10
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• pp.1325-1337
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• 2008

The difference of environments between training and recognition is the major reason of degradation of speech recognition. To solve this mismatch of environments, various noise processing methods have been studied. Among them, ERN(log-Energy dynamic Range Normalization) and SEN(Silence Energy Normalization) for normalization of log energy features show better performance than others. However, these methods have a problem that they can hardly achieve normalization for the relatively higher values of log energy features and the environmental mismatch caused by this problem becomes bigger especially in low SNR environments. To solve these problems, we propose applying ARMA filter as post-processing for smoothing log energy features by calculating the moving average in auto-regression scheme. From the recognition results conducted on Aurora 2.0 DB, the proposed method shows improved recognition results comparing with conventional methods.

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.193-194
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• 2005

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.97-103
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• 2012

In this paper, we propose a novel voice activity detection (VAD) algorithm to effectively distinguish speech from nonspeech in various noisy environments. Global speech absence probability (GSAP) derived from likelihood ratio (LR) based on the statistical model is widely used as the feature parameter for VAD. However, the feature parameter based on conventional GSAP is not sufficient to distinguish speech from noise at low SNRs (signal-to-noise ratios). The presented VAD algorithm utilizes GSAP based on Teager energy (TE) as the feature parameter to provide the improved performance of decision for speech segments in noisy environment. Performances of the proposed VAD algorithm are evaluated by objective test under various environments and better results compared with the conventional methods are obtained.