• 한국정보통신학회논문지
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• 제24권10호
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• pp.1376-1383
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• 2020

화재 대피시스템에 관한 연구가 증가하고 있지만, 실내공간에서 대피자의 위치 인식에 관한 연구는 부족한 실정이다. 최신 연구에 따르면 실내에서 대피자 위치 파악에 고주파 활용이 효과적일 수 있음을 제시한 바가 있다. 이에 따라 본 논문에서는 고주파 특성과 스마트폰을 활용한 대피자 위치 인식 기술 및 화재 대피 안내시스템을 개발하고자 한다. 전체 시스템은 앱 서버, 위치 인식부, 대피경로 탐색 및 출력부, Wi-Fi통신 기반의 스피커 출력부를 포함해 개발했으며, 화재 상황 데이터를 기반으로 실험을 수행하여 시스템의 실효성에 대한 가능성을 입증하였다. 본 연구는 화재 시 고주파를 활용한 대피자 위치 감별을 사용하는 화재 대피 안내시스템의 기초연구로 활용될 수 있을 것이라 기대된다.

• 대한전기학회논문지:전력기술부문A
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• 제55권6호
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• pp.249-254
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• 2006

A new algorithm for estimating power frequency is presented. Unlike conventional algorithms, the proposed algorithm is based on the fact that the magnitude gains of cosine and sine filters become different when the power frequency is deviated from the nominal value. This makes the algorithm capable of providing an accurate and fast estimate of the power frequency. To demonstrate the performance of the developed algorithm, various computer simulated data records are processed. The algorithm showed a high level of robustness as well as high measurement accuracy over a wide range of frequency changes. Moreover, the algorithm was highly immune to harmonics and noise.

• 상하수도학회지
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• 제34권1호
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• 한국통신학회논문지
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• 제27권10C호
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• pp.987-992
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• 2002

본 논문에서는 ㎓대역의 고속 클럭 신호를 필요로 하는 데이터 통신 시스템 분야에 응용될 수 있는 새로운 구조의 클럭 및 데이터 복원회로를 구현하였다. 구현된 회로는 고속 데이터 전송시 주로 사용되는 NRZ형태의 데이터 복원에 적합한 구조로서 위상동기 회로에 발생하는 high frequency jitter를 방지하기 위한 새로운 위상 검출 구조를 갖추고 있다. 또 가변적인 지연시간을 갖는 delay cell을 이용한 위상검출기를 이용하여 위상 검출기가 갖는 dead zone 문제를 해결하고, 항상 최적의 동작을 수행하여 빠른 동기 시간을 갖는다. 수십 Gbps급 대용량을 수신할 수 있도록 다채널 확장에 용이한 구조를 사용하였으며, 1.25Gbps급 데이터를 복원하기 위한 클럭 생성을 목표로 하여 CMOS 0.25$\mu\textrm{m}$ 공정을 사용하여 구현한 후 그 동작을 측정을 통해 검증하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(2)
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• pp.53-56
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• 2001

With the ever increasing clock frequency and integration level of CMOS circuits, I/O(input/output) and interconnect issues are becoming a growing concern. In this thesis, we propose the 2.5Gbps high speed input driver This driver consists of four different blocks, which are the high speed serializer , PECL(pseudo emitter coupled logic) Line Driver, PLL(phase lock loop) and pre-emphasis signal generator. The proposed pre-emphasis block will compensate the high frequency components of the 2.5Gbps data signal. Using the pre-emphasis block, we can obtain 2.5Gbps data signal with differential peak to peak voltage about 900 m $V_{p.p}$ This driver structure is on fabrication in 2.5v/10.25um 1poly, 5metal CMOS process.

• 한국표면공학회지
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• 제45권1호
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• pp.43-52
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• 2012

Recently, it has been developed for Eco-environment, Super light, Multi-functional nano materials. As needed mobile parts in Smart phone or TV, computer, information communication for high pass signal, multi-function, Magnetic thin film materials have been developed. As last, magnetic powder, sintered and sputtering parts were thick and low purity than electroplating layer, low pass signal and noise were resulted, vibrated TV screen. Because chemical complex temperature was high and ununiform surface layer, it has been very difficult for data pass in High Frequency (GHz) area. Large capacity data pass is used to GHz. Above GHz, signal pass velocity is dependent on Skin Effect of surface layer. If surface layer is thick or ununiform, attachment is poor, low pass signal and cross talk, noise are produced and leaked. It has been reported technical trend of Electrochemically plating and Surface treatment of Metal, Polymer, Ceramic etc. by dispersion/complex for Multi functional nano-magnetic material in this paper.

• Structural Engineering and Mechanics
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• 제68권1호
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• pp.39-51
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• 2018

In this study, a frequency domain model updating method is presented using power spectral density (PSD) data. It uses the sensitivity of PSD function with respect to the unknown structural parameters through a decomposed form of transfer function. The stiffness parameters are captured with high accuracy through solving the sensitivity equations utilizing the least square approach. Using numerically noise polluted data, the model updating results of a truss model prove robustness of the method against measurement and mass modelling errors. Results prove the capabilities of the method for parameter estimation using highly noise polluted data of low ranges of excitation frequency.

• Corrosion Science and Technology
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• 제2권2호
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• pp.93-97
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• 2003

Fatigue crack growth test or low alloy steel was performed in high temperature water. Test parameters were dissolved oxygen content. loading frequency and R-ratio ($P_{min}/P_{max}$). Since the sulfur content or the steel was low, there were no environmentally assisted cracks (EAC) in low dissolved oxygen(DO) water. At high DO, the crack growth rate at R = 0.5 tests was much increased due to environmental effects and the crack growth rate depended on loading frequency and maximized at a critical frequency. On the other hand, R = 0.7 test results showed an anomalous decrease of the crack growth rate as much different behavior from the R = 0.5. The main reason of the decrease may be related to the crack tip closure effect. All the data could be qualitatively understood by effects of oxide rupture and anion activity at crack tip.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.61-62
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• 2022

The unit quantity is an important factor influencing the durability, workability, and quality of concrete. Methods for measuring the unit quantity include a high frequency heating method, a unit volume mass method, a capacitance method, and a microwave method. However, these methods have disadvantages of poor measurement method, time required, and accuracy, and a relatively experimental method compensating for these disadvantages was used to measure the unit quantity using a high frequency main sensor (FDR) capable of simple and fast measurement. In addition, the unit quantity was evaluated by analyzing the measurement data through deep learning.

• 한국의상디자인학회지
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• 제24권3호
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• pp.1-15
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• 2022

The purpose of this study is to compare and analyze the perception of men's fashion before and after the COVID-19 pandemic. TEXTOM allowed the collection of Big Data based on the term 'men's fashion'. As for the data collection periods, Jan. 1, 2018 to Dec. 31, 2019 was set as the pre-COVID-19 era, while Jan. 1, 2020 to Dec. 31, 2021 was set as the post-COVID-19 era. The top 50 words in terms of appearance frequency were extracted from the data. The extracted words were processed using network centrality analysis and CONCOR analysis using Ucinet 6. Research findings were as follows. 1) In the pre-COVID-19 era, the appearance frequency of 'men' was the highest, followed by 'fashion', 'men's fashion', 'brand', 'daily look', 'suit', and 'department store'. These words came up with a high TF-IDF values. Network centrality analysis discovered that 'men', 'fashion', 'men's fashion', 'brand', and 'suit' had a high level of connectivity with other words. CONCOR analysis showed four significant groups: 'fashion item and styles', 'fashion show', 'purchase', and 'collection'. 2) In the post-COVID-19 era, the appearance frequency of 'men' was the highest, followed by 'fashion', 'brand', 'men's fashion', 'discount', 'women', and 'luxury'. These words also displayed high TF-IDF values. Network centrality analysis found that 'fashion', 'men', 'brand', 'men's fashion', and 'discount' had a high level of connectivity with other words. CONCOR analysis showed four significant groups: 'fashion item and style', 'fashion show', 'purchase', and 'situation'. 3) Before the outbreak of the pandemic, men were interested in suits to wear to the office, daily look, and fashion shows in Milan and Paris. They often purchased menswear in multi-brand and open stores. However, they were more interested in sneakers, casual styles, and online fashion shows as social distancing and working from home became common. Most purchased menswear through online platforms.