• 방송공학회논문지
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• 제13권6호
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• pp.849-858
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• 2008

본 논문에서는 열화된 영상의 화질을 평가함에 있어서 원본 영상을 참조할 필요가 없는 객관적인 평가 방법을 제안한다. 제안하는 알고리듬은 블록 기반의 손실 부호화 과정에서 발생 가능한 블록형 잡음 및 뭉개짐 현상의 정도를 정량화하며, 이와 함께 강한 에지 주변에서 특징적으로 나타나는 물결형 떨림, 계단형 떨림 및 모자이크 잡음 등을 정량화한다. 그리고 퍼지 적분을 이용하여 각각의 잡음의 정도를 통합하여 최종적인 점수를 계산함으로써 주어진 영상의 화질을 평가한다. 제안 알고리듬에 따라 얻어진 화질 평가 결과는 전문가 집단에 의한 주관적 화질 평가 결과와 높은 유사성을 보인다.

• 품질경영학회지
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• 제45권4호
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• pp.649-664
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• 2017

Purpose: This paper introduces the technology of prognostics for Industry 4.0 and presents its application procedure for fitness-for-service assessment of natural gas pipelines according to ISO 13374 framework. Methods: Combining data-driven approach with pipe failure models, we present a hybrid scheme for the gas pipeline prognostics. The probability of pipe failure is obtained by using the PCORRC burst pressure model and First Order Second Moment (FOSM) method. A fuzzy inference system is also employed to accommodate uncertainty due to corrosion growth and defect occurrence. Results: With a modified field dataset, the probability of failure on the pipeline is calculated. Then, its residual useful life (RUL) is predicted according to ISO 16708 standard. As a result, the fitness-for-service of the test pipeline is well-confirmed. Conclusion: The framework described in ISO 13374 is applicable to the RUL prediction and the fitness-for-service assessment for gas pipelines. Therefore, the technology of prognostics is helpful for safe and efficient management of gas pipelines in Industry 4.0.

• Structural Engineering and Mechanics
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• 제23권5호
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• pp.455-468
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• 2006

Smart structural systems are defined as ones that demonstrate the ability to modify their characteristics and/or properties in order to respond favorably to unexpected severe loading conditions. The performance of such a task requires a set of additional components to be integrated within such systems. These components belong to three major categories, sensors, processors and actuators. It is wellknown that all structural systems entail some level of uncertainty, because of their extremely complex nature, lack of complete information, simplifications and modeling. Similarly, sensors, processors and actuators are expected to reflect a similar uncertain behavior. As it is imperative to be able to evaluate the impact of such components on the behavior of the system, it is as important to ensure, or at least evaluate, the reliability of such components. In this paper, a system model for reliability assessment of smart structural systems is outlined. The presented model is considered a necessary first step in the development of a reliability assessment algorithm for smart structural systems. The system model outlines the basic components of the system, in addition to, performance functions and inter-relations among individual components. A fault tree model is developed in order to aggregate the individual underlying component reliabilities into an overall system reliability measure. Identification of appropriate limit states for all underlying components are beyond the scope of this paper. However, it is the objective of this paper to set up the necessary framework for identifying such limit states. A sample model for a three-story single bay smart rigid frame, is developed in order to demonstrate the proposed framework.

• 한국건설관리학회논문집
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• 제6권4호
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• pp.164-171
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• 2005

최근 생애주기비용 분석의 중요성이 대두됨에 따라 공동주택 리모델링 사업에서의 생애주기비용 분석을 위한 이론적 모델, 표준지침 및 소프트웨어 등이 개발되어지고 있다. 그러나 현재까지도 실질적인 안전진단에 대한 과거 보수이력데이터의 부재로 인한 LCC분석 결과에 대한 신뢰수준이 여전히 미흡하다. 본 연구에서는 사례 공동주택의 신뢰성에 기초한 LCC분석을 통해 리모델링 전략을 평가하고, 장래 요구되는 리모델링 조치수준의 비교 $\cdot$ 분석을 통해 적정 경제성지수를 제시함과 동시에 최근 연구가 활발히 진행 중인 공동주택 리모델링에 대한 실적 LCC 기초자료를 얻고자 하였다. 본 연구에서 제안된 퍼지로직에 기초한 안전성평가와 LCC분석모델은 공동주택 리모델링사업에서의 가치 지향적 설계대안 선정, 경제성평가 및 합리적인 예산의 분배 등에 크게 기여할 것으로 기대된다.

• Journal of Biosystems Engineering
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• 제42권1호
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• pp.23-43
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• 2017

Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance.

• Computers and Concrete
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• 제16권6호
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• pp.919-932
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• 2015

This paper presents a new method for assessing the three-point-bending (3PB) strength of mortar beams in a non-destructive manner, based on neural network (NN) models. The models are based on the radial basis function (RBF) architecture and the fuzzy means algorithm is employed for training, in order to boost the prediction accuracy. Data for training the models were collected based on a series of experiments, where the cement mortar beams were subjected to various bending mechanical loads and the resulting pressure stimulated currents (PSCs) were recorded. The input variables to the NN models were then calculated by describing the PSC relaxation process through a generalization of Boltzmannn-Gibbs statistical physics, known as non-extensive statistical physics (NESP). The NN predictions were evaluated using k-fold cross-validation and new data that were kept independent from training; it can be seen that the proposed method can successfully form the basis of a non-destructive tool for assessing the bending strength. A comparison with a different NN architecture confirms the superiority of the proposed approach.

• 한국컴퓨터산업학회논문지
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• 제8권3호
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• pp.147-154
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• 2007

각 급 학교에서는 학습이 끝난 후에 실시하는 총괄평가의 결과만으로 학습평가를 하고 있는데 이러한 평가 방식은 학습자의 학습능력의 형성과정을 고려하지 않는 결과위주의 학습평가로 볼 수 있다. 또 기존의 학습평가는 학습 수행능력을 판정하기 위한 진단평가와 학습능력의 향상 정도를 측정하기 위한 형성평가를 각기 개별적으로 수행하여 평가하기 때문에 학습 수행능력을 보다 명확하게 처리하기 곤란한 점이 있다. 따라서 본 논문에서는 학습자의 능력을 보다 객관적으로 평가하기 위한 방안으로 퍼지 추론을 이용하여 진단평가와 형성평가를 통합 평가할 수 있는 학습평가 방법을 제안한다.

• 한국지능시스템학회논문지
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• 제22권1호
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• pp.75-80
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• 2012

거동 이상 선박(갈지자 항행 선박, 제자리 순회 선박 등)은 정상적인 항로를 유지하는 선박에게 심각한 영향을 미칠 수 있는 요소이며, 현재 육지에 있는 VTS 센터와 해양 경찰이 연계되어 범죄 선박 및 사고 선박을 추적하고 있다. 하지만 인적 요인에 의한 위험 요인 식별의 한계는 명확하며 그를 보조할 수 있는 연구는 거의 없는 실정이다. 따라서, 이 연구에서는 퍼지추론을 이용하여 관제자 및 항해사를 위한 지능형 항해 거동 이상 선박 식별 시스템을 구현하고자 한다.

• Nuclear Engineering and Technology
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• 제54권11호
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• pp.4095-4101
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• 2022

Following the climate-related disasters considered by several efforts, the nuclear capacity needs to double by 2050 compared to 2015. So, it is reasonable to investigate global warming incorporated with the fuzzy set theory for nuclear energy consumption in the aspect of fuzziness and nonlinearity of temperature variations. The complex modeling is proposed for the enhanced assessment of climate change where simulations indicate the degree of influence with the Boolean values between 0.0 and 1.0 in the designed variables. In the case of OIL, there are many 1.0 values between 20^th and 60^th months in the simulations where there are 10 times more for a 1.0 value in influence. Hence, the temperature variable can give the effective time using this study for 100 months. In the analysis, the 1.0 value in NUCLEAR means the highest influence of the modeling as the temperature increases resulting in global warming. In detail, the first influence happens near the 8th month and then there are four times more influences than effects in the early part of the temperature mitigation. Eventually, in the GLOBAL WARMING, the highest peak is around the 20^th month, and then it is stabilized.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.