• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.401-402
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• 2006

In the present paper, approximate plastic limit load solutions for pipe bends under combined internal pressure and bending are obtained from detailed three-dimensional (3-D) FE limit analyses based on elastic-perfectly plastic materials with the small geometry change option. The present FE results show that existing limit load solutions for pipe bends are lower bounds but can be very different from the present FE results in some cases, particularly for bending. Accordingly closed-form approximations are proposed for pipe bends under combined pressure and in-plane bending based on the present FE results. The proposed limit load solutions would be a basis of defective pipe bends and be useful to estimate non-linear fracture mechanics parameters based on the reference stress approach.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.6
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• pp.72-77
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• 2007

A safety-related equipment for the nuclear power plant should be needed an equipment qualification. In this paper, the approach, methods, philosophies, and procedures for qualifying the large squirrel-cage induction electric pump motors for use in ULCHIN 5, 6 Nuclear Power Plants were presented. The method of qualification is a combination of experimental test and analytic method, which is composed of radiation exposure test, seismic simulation test, thermal aging analysis for non-metallic materials, and seismic analysis. The results showed that the motor performed its safety function with no failure mechanism under postulated service conditions.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.2
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• pp.84-91
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• 1997

This paper reports the effort for developing this new Unified Manufacturing Instruction Set and its environment, called here UniSet, to deal with difficulties in set up and operation of Flexible Manufacturing Cells, UniSet has been developed as a non-exclusive unified manufacturing instruction set based on com- parisons of the prevailing machine tool and programming primitives. UniSet allows programmers to deal with only one instruction set, if they so desire, in a single coherent enviroment, rather than numerous machine programming languges. The software system is coded in an Object-Oriented Programming (OOP) language, Smalltalk, and derives its paradigm from the OO philosophy. Test results are also includ- ed to demonstrate the applicability of the approach employed.

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.397-408
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• 2009

In this paper, we present a non-static color histogram method to retrieve clothing images that are similar to a query clothing. Given clothing area, our method automatically extracts major colors by using the octree-based quantization approach[16]. Then, a color palette that is composed of the major colors is generated. The feature of each clothing, which can be either a query or a database clothing image, is represented as a color histogram based on its color palette. We define the match color bins between two possibly different color palettes, and unify the color palettes by merging or deleting some color bins if necessary. The similarity between two histograms is measured by using the weighted Euclidean distance between the match color bins, where the weight is derived from the frequency of each bin. We compare our method with previous histogram matching methods through experiments. Compared to HSV cumulative histogram-based approach, our method improves the retrieval precision by 13.7 % with less number of color bins.

• Computers and Concrete
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• v.19 no.5
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• pp.527-535
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• 2017

In this study, an aging deformation statistical model for a unique high and steep rock slope was proposed, and the aging characteristic of the slope deformation was better reflected. The slope displacement was affected by multiple-environmental factors in multiple scales and displayed the same tendency with a rising water level. The statistical model of the high and steep rock including non-aging factors was set up based on previous analyses and the study of the deformation and residual tendency. The rule and importance of the water level factor as a non-aging unit was analyzed. A partitioned statistical model and mutation model were established for the comprehensive cumulative displacement velocity with the monitoring study under multiple factors and multiple parameters. A spatial model was also developed to reflect and predict the whole and sectional deformation character by combining aging, deformation and space coordinates. A neural network model was built to fit and predict the deformation with a high degree of precision by mastering its feature of complexity and randomness. A three-dimensional finite element model of the slope was applied to approach the structure character using numerical simulations. Further, a three-dimensional finite element model of the slope and dam was developed, and the whole deformation state was analyzed. This study is expected to provide a powerful and systematic method to analyze very high, important and dangerous slopes.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.221-227
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• 2007

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and a particle filtering(PF) algorithm to estimate the probability density function(pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life(RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.

• Steel and Composite Structures
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• v.48 no.5
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• pp.583-597
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• 2023

This paper introduces a novel approach to multi-material topology optimization (MTO) targeting in-plane bi-directional functionally graded (IBFG) non-uniform thickness Reissner-Mindlin plates, employing an alternative active phase approach. The mathematical formulation integrates a first shear deformation theory (FSDT) to address compliance minimization as the objective function. Through an alternating active-phase algorithm in conjunction with the block Gauss-Seidel method, the study transforms a multi-phase topology optimization challenge with multi-volume fraction constraints into multiple binary phase sub-problems, each with a single volume fraction constraint. The investigation focuses on IBFG materials that incorporate adequate local bulk and shear moduli to enhance the precision of material interactions. Furthermore, the well-established mixed interpolation of tensorial components 4-node elements (MITC4) is harnessed to tackle shear-locking issues inherent in thin plate models. The study meticulously presents detailed mathematical formulations for IBFG plates in the MTO framework, underscored by numerous numerical examples demonstrating the method's efficiency and reliability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.818-821
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• 2004

With a rapid development in the area of micro and ultra precision technology, the micro surface machining of small size parts are explosively increased. Especially, to improve efficiency of various beams in lens and reflector, non-rotational symmetric form and several mm level heights changeable surface can be machined at a time. These geometric complex 3D surface cannot be machined by general short stroke FTS. The long stroke FTS if firmly needed to move directly several mm and have nm level positioning accuracy for the complex surface form. The long stroke FTS used linear motors to drive moving unit long and fine, aero static bearings to decrease friction and moving errors in guide way, optical linear scale with nm level resolution to measure position of FTS. Furthermore, to increase the performance of acceleration of FTS, the light material, such as AL is used for the structure and the high stiffness box type structure is selected. In this paper, the genetic algorithm approach is described to determine a set of design parameters for auto tuning. The authors have attempted to model the design problem with the objective of minimizing the error, such as variable pattern change. This method can give the better alternative than existing other method.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.