• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.849-858
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• 2008

In this paper, a no-reference perceptual metric is proposed for image quality assessment. It measures the amount of overall blockiness and blurring of the image and evaluates the amount of ringing, staircase, and mosaic noises around the strong edges. Finally, the individual scores are combined by a fuzzy integral to generate the final quality score of the image. The quality scores obtained by the proposed algorithm show strong relationship with the MOS(Mean Opinion Score) values by experts.

• Journal of Korean Society for Quality Management
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• v.45 no.4
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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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• v.23 no.5
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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.

• Korean Journal of Construction Engineering and Management
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• v.6 no.4 s.26
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• pp.164-171
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• 2005

The importance of the life cycle cost analysis(LCCA) for apartment housing remodeling projects has been fully recognized over the last decade. Accordingly theoretical models, guidelines, and supporting software systems were developed for the life cycle cost analysis of apartment housing remodeling systems. However, the level of consensus on LCCA results is still low due to the lack of reliable data on remodeling activities for safety diagnosis. in order to predict the reliability based LCCA of the given case, suggested the remodeling strategies level after reviewing other related materials. Apply the real information of the economic index. And based on such analytical measures, remodeling and operation cost and LCC in remodeling strategies level have been predicted; suggests the basic information about remodeling interventions level for the apartment housing. The LCC analysis models and the fuzzy logic based safety assessment presented in this study can greatly contribute to the value-oriented design alternative selection, estimation of the economic analysis, and the allocation of budget for apartm.

• Journal of Biosystems Engineering
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• v.42 no.1
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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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• v.16 no.6
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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.

• Journal of the Korea Computer Industry Society
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• v.8 no.3
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• pp.147-154
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• 2007

Many studies have reported that each evaluation had a stronger effect on the development of a student's teaming ability. Nevertheless, in reality schools rely on the results of summative evaluation after the lesson only for the purpose of learning evaluation. Such a method of evaluation is a result-oriented learning evaluation, with no consideration of developing process of loaming ability of each student. Existing learning evaluation has been considered difficult to process learning performance ability in a clearer manner, as it examines teaming performance ability by diagnostic evaluation and learning ability improvement by formative evaluation, separately. Therefore, this paper proposes a learning evaluation method incorporating diagnostic and formative evaluation, using a Fuzzy inference, for a more objective assessment of performance ability. The proposed method assessed teaming ability based on different weight values, in order to reflect the level of diagnostic and formative evaluation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.75-80
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• 2012

Abnormal navigating ships affact the ships navigating normal routes seriously. So VTS centers and Korean Coast Guard co-work(cooperate) closely to trace the ships which break the regulations and make accidents. But it is evident that there is limitations to indetify the risk factors caused by men. Unfortunately there is very few of the researches on the identificaton of risk elements by men. This paper is to implement the intelligent system for identifying abnormal navigating ships by using fuzzy inference.

• Nuclear Engineering and Technology
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• v.54 no.11
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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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• v.23 no.5
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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%.