• 품질경영학회지
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• 제46권3호
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• pp.651-658
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• 2018

Purpose: The algorithm described in Lim(1998) is available to determine the sample size directly given specified significance level, power and signal-to-noise ratio. We research on the efficient determination of the sample size by visual methods. Methods: We propose three graphs for investigating the mutual relationship between the sample size r, power $1-{\beta}$ and the detectable signal-to-noise ratio ${\Delta}$. First graph shows the relationship between ${\Delta}$ and $1-{\beta}$ for the given r and it can be checked whether the power is sufficient enough. Second graph shows the relationship between r and ${\Delta}$ for the given power $1-{\beta}$. Third graph shows the relationship between r and $1-{\beta}$ for the given ${\Delta}$. It can be checked that which effects are sensitive to the efficient sample size by investigating those graphs. Results: In factorial design, randomized block design and the split plot design how to determine the sample size directly given specified significance level, power and signal-to-noise ratio is programmed by using R. A experiment to study the split plot design in Hicks(1982) is used as an example. We compare the sample sizes calculated by randomized block design with those by split plot design. By using graphs, we can check the possibility of reducing the sample size efficiently. Conclusion: The proposed visual methods can help an engineer to make a proper plan to reduce the sample size.

• Smart Structures and Systems
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• 제22권2호
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• pp.185-192
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• 2018

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain ($QA_{HSS}$), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

• 한국건설관리학회논문집
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• 제9권5호
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• pp.176-185
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• 2008

도심이 과밀화됨에 따라 재건축 및 재개발의 활성화, 대규모 택지개발에 의한 신도시 개발, 사회기반시설 확충 등으로 건설폐기물이 급증하고 있다. 이와 더불어 도심지내 공사장 등에 의한 소음, 진동 및 분진 발생원이 지속적으로 증가하고 다양화됨에 따라 이와 관련된 환경 분쟁 및 민원이 급증하고 있는 추세이다. 이에 따라 정부는 생활주변의 정온한 환경을 조성하기 위하여 가까운 미래에 각종관련기준을 상향 적용할 계획이며, 결과적으로 건설공사관련 환경 분쟁 및 민원의 발생이 더욱 증가할 것으로 예상된다. 따라서 본 연구에서는 현장파쇄시설을 운영하고 있는 현장에서의 소음, 진동, 분진 발생량을 측정하고 분석하여, 현장파쇄시설에서 발생되는 환경민원발생 예방측면에서 대책을 마련하여 건설폐기물의 현장재활용 활성화를 위한 기초 자료를 제시하고자 한다.

• 한국건설관리학회논문집
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• 제21권4호
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• pp.12-20
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• 2020

도로 하부에 매설된 지하매설물에 대한 유지보수의 수요는 지속적으로 크게 증가하고 있으며, 과다한 소음 및 분진으로 인한 환경 문제 및 도심지 주민들의 민원으로 인해 친환경·저소음 도로 절단기에 대한 수요가 점차 증대되고 있다. 이에 따라 현재 사용되고 있는 저소음 절단기와 현재 개발되고 있는 친환경·저소음 절단기에 대한 절단 성능을 분석하고 개선사항을 검토해보자고 한다. 이를 위해 현장시공 사례를 바탕으로 절단기 작업절차에 따른 문제점과 개선 사항들을 도출하였다. 현장시공 사례 분석 결과, 절단 작업에 대한 생산성은 친환경·저소음 절단기(36.0초/m)가 저소음 절단기(37.6초/m)에 비해 다소 생산성이 향상되었으나 일부 구간에 결함이 발생되어 이에 대한 개선이 요구되었다.

• Journal of Animal Science and Technology
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• 제65권2호
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• 웰빙융합연구
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• 제5권4호
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• pp.19-31
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• 2022

Purpose: This study was conducted to identify indoor air quality in various living spaces using sensors that can measure noise, vibration, fine dust, and odor in real time and to propose optimal indoor air quality maintenance management using Internet of Things(IoT). Research design, data and methodology: Using real-time sensors to monitor physical factors and environmental air pollutants that affect the comfort of the residential environment, Noise, Vibration, Atmospheric Pressure, Blue Light, Formaldehyde, Hydrogen Sulfide, Illumination, Temperature, Ozone, PM10, Aldehyde, Amine, LVOCs and TVOCs were measured. It were measured every 1 seconds from 4 offices and 4 stores on a small scale from November 2018 to January 2019. Results: The difference between illuminance and blue light for each measuring point was found to depend on lighting time, and the ratio of blue light in total illumination was 0.358 ~ 0.393. Formaldehyde and hydrogen sulphide were found to be higher than those that temporarily attract people in an indoor office space that is constantly active, requiring office air ventilation. The noise was found to be 50dB higher than the office WHO recommendation noise level of 35 ~ 40dB. The most important factors for indoor environmental quality were temperature> humidity> illumination> blue light in turn. Conclusions: Various factors that determine the comfort of indoor living space can be measured with real-time sensors. Further, it is judged that the use of IoT can help maintain indoor air quality comfortably.

• 한국환경생태학회지
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• 제36권3호
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• pp.350-357
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• 2022

본 연구에서는 국립공원에서 자연의 소리는 생태계의 구성요소임과 동시에 탐방객들이 공원을 체험하는 중요한 요소이다. 따라서 국립공원 내 소음 분포 특성을 파악하고 자연의 음환경(Soundscape) 보존 및 복원을 위한 노력은 국립공원 관리 차원에서 중요한 과제 중 하나이다. 그러나 여전히 국내 국립공원에서는 소음 관리 및 음환경 보존에 대한 관심이 미약한 것이 사실이다. 무등산국립공원은 호남지역에서 가장 중요한 자연보호지역 중 하나로 계절별 동물이 우는 소리, 물소리, 바람 소리 등 탐방객들에게 자연의 소리를 제공할 수 있는 최적의 장소이지만 대도시에 인접해 있고 공원 내부로 도로 및 대규모 시설지가 인접해있는 등 소음이 발생하여 공원 내 음환경에 위협이 되고 있는 상황이다. 따라서 본 연구는 소음지도, 탐방로 소리등급도, 주요 지점의 음환경 녹음 데이터, 소리자원 분포도 등의 분석을 통해 무등산국립공원 내 소리 분포 특성을 파악하고 광범위한 소음의 영향을 나타냄으로써 음환경 관리의 필요성을 시사하고 있다는 중요성을 지니고 있다. 반면 본 연구에서는 연구 인력과 장비의 한계로 인하여 소음지도, 탐방로 소리등급도, 소리자원 분포도의 속성 데이터 수집 기간이 상이한 점이 한계점으로 남는다. 그러나 본 연구의 결과는 탐방 만족도 및 자연의 음환경 보존 등을 목표로 향후 지속적인 음환경 보존 및 관리를 위해 활용될 수 있는 기초자료가 될 것으로 판단된다.

• 한국수자원학회논문집
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• 제57권3호
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• pp.209-223
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• 2024

4차 산업혁명 시대에 접어들어 데이터 기반의 의사결정이 보편화되고 있다. 하지만 데이터 품질이 확보되지 않은 채 수행되는 데이터 분석은 왜곡된 결과를 낳을 가능성이 존재한다. 수자원 관리의 기초가 되는 수위 데이터도 마찬가지로 결측, 스파이크, 잡음 등 다양한 품질 문제를 가진다. 본 연구에서는 잡음으로 인해 발생하는 데이터 품질 문제를 해결하고자 하였다. 잡음은 데이터의 트렌드 분석을 어렵게 하고 비정상적인 이상치를 생성할 가능성이 있다. 본 연구는 이러한 문제를 해결하기 위해 Wavelet Transform을 이용한 잡음 제거 접근 방안을 제안한다. Wavelet Transform은 신호처리에 주로 사용되는 방법으로 잡음 제거에 효과적인 것으로 알려져 있으며 수집된 데이터의 정답 데이터(True value) 수집을 요구하지 않으므로 시간과 비용을 줄일 수 있다는 점에서 적용이 용이한 편이다. 본 연구는 Wavelet Transform의 성능 평가를 위해 대표적인 머신러닝 기반 잡음 제거 방법인 Denoising Autoencoder와 성능 비교를 수행하였다. 그 결과 Wavelet Transform 중 Coiflets 함수는, Denoising Autoencoder에 비해 Mean Absolute Error, Mean Absolute Percentage Error, Mean Squared Error 등 모든 측면에서 우수한 성능을 보이는 것으로 나타났다. 이러한 결과는 환경에 맞는 적절한 웨이블릿 함수의 선택을 통한 잡음 문제를 효과적으로 해결할 수 있음을 시사한다. 본 연구는 수위 데이터의 품질을 향상시켜 수자원 관리 결정의 신뢰성에 기여하는 강력한 도구로서 Wavelet Transform의 잠재력을 확인한 의의가 있다.

• 한국컴퓨터정보학회논문지
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• 제22권6호
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• pp.149-156
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• 2017

In this paper, we propose to enhance a Physical working conditions to decrease absence from work. we demonstrated the correlation of worker's absence with various environmental factors in workplace by surveying 6,962 workers. As result, first, concerning exposure to physical work factors, most of them complained of vibration, and concerning exposure to improper working posture, most of them complained of repetitive arm and hand motions. Second, the absence experience had correlations with age, monthly income, smoking, number of employees, long term working, and shift work, and of the physical factors, hand vibration, noise, high temperature, low temperature and improper working posture had correlations with physical pain posture, movement of people and carrying heavy materials. Third, experience of exposure to hand vibration, noise and low temperature of the physical factors had impact on absence, and of the improper working posture, physical pain posture and carrying heavy stuff had impacts on absence. Through this study, it was found that of the work factors of Korean workers, physical factors and improper working posture had impacts on absence. The results of this study confirmed that physical factors and inappropriate working posture among work factors influenced the absenteeism. Therefore, it is required to improve the work environment regarding physical risk factors and prepare a systematic management plan.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.137-142
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• 2018

The purpose of this study is to contribute to the improvement of indoor air quality management in dental clinic by investigating the level of indoor air quality recognition among dental clinic workers. The questionnaire survey was conducted for about 4 weeks from May 20 to June 20, 2018 in dental clinics located in Jeollanamdo area and 143 were used as the analysis data. The method of indoor air quality management in dental clinic was preferred to "natural ventilation" method and the number of natural ventilation was 1 to 2 times per day and the results of survey on indoor environment satisfaction showed that satisfaction level was lowest in noise and smell items. The types of subjective symptoms experienced by workers working at dental clinics are "cough", "eye burn", and "headache" and a survey on the degree of the relationship between subjective symptoms and indoor air quality showed that 94.4% (135) of respondents answered "very relevant" and "slightly related". As a result of multiple regression analysis, the variables affecting the indoor air quality satisfaction of the dental clinic staff were analyzed as the items such as lighting, noise, main work, number of patients, comparing indoor and outdoor air quality and among them, "comparing indoor and outdoor air quality" was analyzed as having a great influence. To improve the indoor air quality satisfaction of dental clinic worker adequate ventilation, designate the person responsible for the indoor air quality management and periodic measurement efforts will be necessary.