• 한국정보전자통신기술학회논문지
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• 제8권3호
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• pp.239-242
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• 2015

시각장애인들은 외출을 하기 위해 보행 보조기기를 사용하고 있다. 또한, 최근에는 보행 보조기기뿐만 아니라 음성인식 명령기능을 탑재한 스마트폰도 이용하고 있다. 이러한 추세에 따라, 본 논문에서는 시각장애인 보행 보조를 위한 스마트 폰 케이스를 설계하고 구현하였다. 조도 센서와 스마트폰 카메라 플래시를 이용하여 어두운 장소에서 자신의 위치를 알려주는 자기 위치 알림 시스템과 초음파 센서를 이용하여 장애물을 감지하고 시각장애인들에게 음성으로 경고를 해주는 음성 경고 시스템을 제공한다. 이를 이용하면 시각장애인은 어두운 곳에서 자신의 위치를 알리고, 전방의 장애물을 피해갈 수 있어서 보다 안전하게 보행하여 사고를 방지할 수 있다.

• 한국산학기술학회논문지
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• 제16권2호
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• pp.1398-1402
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• 2015

표면실장기술에 있어서, 자동차 정션박스 등에 삽입되는 릴레이를 칩 마운터를 이용하여 실장 하는 기술이 대두되고 있다. 그러나 릴레이가 일반 칩과 다르게 스틱 튜브로 공급되고 부품의 무게가 무거워 공급하는 기술이 필요하다. 따라서 본 연구는 스틱 튜브를 이용한 부품 공급 장치를 기존 기술보다 보다 안정적으로 공급을 할 수 있는 에어를 이용한 기계적 구조를 마련하고, 이를 활용한 시스템 알고리즘을 개선하는 기술을 제시한다. 또 개선된 에어 스틱 피더를 설비에 장착하여 사용했을 때 생산량의 증가와 폐기 비용 감소 효과를 확인할 수 있다.

• 융합신호처리학회논문지
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• 제20권1호
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• pp.57-62
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• 2019

본 논문에서는 회전기기의 전압, 전류, 온도, 가속도, 진동 등을 측정 전송할 수 있는 oneM2M 기반의 실시간 회전기기 센싱 데이터 수집 및 모니터링 시스템을 설계하고 구현하였다. 구현된 시스템은 전기적 결함(과전류, 역상, 결상, 지락)과 기계적 결함(MC 카운터, 모터동작시간, 베어링 및 권선온도, 모터 회전수, 절연저항)의 전기 또는 물리적인 현상 측정이 가능하며, 센서데이터 수집, 웹서버, php, 데이터베이스에 데이터 저장, 웹 접속 통한 데이터 모니터링까지 가능하도록 시스템을 구성하였다. 회전기기에서의 기계적 결함을 실험한 결과, 절연저항 및 모터회전수 측정 결과 시험저항 값과 기준 입력값 각각에서 유사한 실험 결과를 보였다.

• Smart Structures and Systems
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• 제7권5호
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• pp.365-378
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• 2011

Piezo-based active structural health monitoring (SHM) requires amplifiers specifically designed for capacitive loads. Moreover, with the increase in number of applications of wireless SHM systems, energy efficiency and cost reduction for this type of amplifiers is becoming a requirement. General lab grade amplifiers are big and costly, and not built for outdoor environments. Although some piezoceramic power amplifiers are available in the market, none of them are specifically targeting the wireless constraints and low power requirements. In this paper, a piezoceramic transducer amplifier for wireless active SHM systems has been designed. Power requirements are met by two digital On/Off switches that set the amplifier in a standby state when not in use. It provides a stable ${\pm}180$ Volts output with a bandwidth of 7k Hz using a single 12 V battery. Additionally, both voltage and current outputs are provided for feedback control, impedance check, or actuator damage verification. Vibration control tests of an aluminum beam were conducted in the University of Houston lab, while wireless active SHM tests of a wind turbine blade were performed in the Harbin Institute of Technology wind tunnel. The results showed that the developed amplifier provided equivalent results to commercial solutions in suppressing structural vibrations, and that it allows researchers to perform active wireless SHM on moving objects with no power wires from the grid.

• Elastomers and Composites
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• 제45권4호
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• pp.291-297
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• 2010

차량에서 스트러트 인슐레이터는 도로노면으로부터 발생하는 소음과 진동을 방지하는 역할을 한다. 대부분의 점탄성 마운트들은 고무로 되어있으며 천연고무가 주성분이다. 이러한 고무 부품들은 초기에는 제 역할을 하지만 오랜 시간 고온과 반복하중에 노출되면 성능저하가 일어난다. 고무성능 변화는 NVH를 떨어뜨리고 승차감을 저하시킨다. 본 연구에서는 차량실험을 통해 스프링 변위를 측정하였고, 재현실험에서 가속도 센서를 이용하여 운행거리와 차량연식에 따른 인슐레이터 고무성능을 가속도 값, 고무 영구 변형량, 경도를 나타내었다.

• Steel and Composite Structures
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• 제29권6호
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Tribology and Lubricants
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• 제36권6호
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• pp.297-306
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• 2020

This paper reviews studies on the types and functions of oil sensors used for machine condition monitoring. Machine condition monitoring is essential for maintaining the reliability of machines and can help avoid catastrophic failures while ensuring the safety and longevity of operation. Machine condition monitoring involves several components, such as compliance monitoring, structural monitoring, thermography, non-destructive testing, and noise and vibration monitoring. Real-time monitoring with oil analysis is also utilized in various industries, such as manufacturing, aerospace, and power plants. The three main methods of oil analysis are off-line, in-line, and on-line techniques. The on-line method is the most popular among these three because it reduces human error during oil sampling, prevents incipient machine failure, reduces the total maintenance cost, and does not need complicated setup or skilled analysts. This method has two advantages over the other two monitoring methods. First, fault conditions can be noticed at the early stages via detection of wear particles using wear particle sensors; therefore, it provides early warning in the failure process. Second, it is convenient and effective for diagnosing data regardless of the measurement time. Real-time condition monitoring with oil analysis uses various oil sensors to diagnose the machine and oil statuses; further, integrated oil sensors can be used to measure several properties simultaneously.

• Structural Monitoring and Maintenance
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• 제8권4호
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.796-801
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• 1994

Measuring noise, sound quality or acoustical comfort presents a difficult task for the acoustic engineer. Sound and noise are ultimately jugded by human beings acting as analysers. Regulations for determining noise levels are based on A-weighted SPL measurement performed with only one microphone. This method of measurement is usually specified when determining whether the ear can be physically damaged. Such a simple measurement procedure is not able to determine annoyance of sound events or sound quality in general. For some years investigations with binaural measurement analysis technique have shown new possibilities for the objective determination of sound quality. By using Artificial Head technology /1/, /2/ in conjunction with psychoacoustic evaluation algorithms - and taking into account binaural signal processing of human hearing, considerable progress regarding the analysis of sounds has been made. Because sound events often arise in a complex way, direct conclusions about components subjectively judged to be annoying with regard to their causes and transmission paths, can be drawn in a limited way only. A new procedure, complementing binaural measurement technology combined with mulit-channel measuements of acceleration sensor signals has been developed. This involves correlating signals influencing sound quality, analyzed by means of human hearing, with signals form different acceleration sensors fixed at different positions of the sound source. Now it is possible to recognize the source and the transmission way of those signals which have an influence on the annoyance of sound.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.82-84
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• 2021

스마트 공장 구축사업을 통해 생산 설비로부터 전력, 진동, 압력, 온도 등의 센서 데이터가 수집되고 있으며 데이터 분석을 통해 예지보전, 불량예측, 이상탐지 등의 서비스 개발이 진행되고 있다. 일반적으로 제조데이터의 경우 정상과 비정상 데이터의 불균형이 극심하여 이상탐지 서비스가 선호되고 있다. 본 논문에서는 이상탐지 서비스 개발의 전단계로 제조데이터의 특징 데이터 추출을 위해 FFT 방법을 사용하였으며, 이를 통해 생산되는 제품을 분류해보고 그 결과를 확인하였다. 즉, 제품별 대표 패턴을 FFT 변환 후 상관계수를 계산하여 제품분류가 가능한지 확인하였다.