• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.4
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• pp.76-81
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• 2011

UV imprinting process can manufacture high-functional optical components with low cost. If hard polymers can be used as transparent molds at this process, the cost will be much lower. However, there are limited researches to predict the machinability and the burr of hard polymers. Therefore, a new method to predict them by analyzing load-depth curves which can be obtained by the instrumented indentation test was developed in this study. The load-depth curve contains elastic deformation and plastic deformation simultaneously. The ratio of the plastic deformation over the sum of the two deformation is proportional to the ductility of materials which is one of the parameters of the machinability and the burr. The instrumented indentation tests were performed on the transparent molds of the hard polymers and the values of ratio were calculated. The machinability and the burr of three kinds of hard polymers were predicted by the ratio, and the prediction was in agreement with the experimental results from the machined surfaces of the three kinds of hard polymers.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.311-316
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• 2017

High Efficiency Video Coding (HEVC) is the newest video coding standard for improvement in video data compression. This new standard provides a significant improvement in picture quality, especially for high-resolution videos. A quadtree-based structure is created for the encoding and decoding processes and the rate-distortion (RD) cost is calculated for all possible dimensions of coding units in the quadtree. To get the best combination of the block an optimization process is performed in the encoder, called rate distortion optimization (RDO). In this work we are proposing a novel approach to enhance the overall RDO process of HEVC encoder. The proposed algorithm is performed in two steps. In the first step, like HEVC, it performs general rate distortion optimization. The second step is an extra checking where a SSIM based cost is evaluated. Moreover, a fast SSIM (FSSIM) calculation technique is also proposed in this paper.

• 지반과기술
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• v.3 no.3
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• pp.15-22
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• 2006

The optimum blasting pattern to excavate a quarry efficiently and economically can be determined based on the minimum production cost, which is generally estimated according to rock fragmentation. Therefore, it is a critical problem to predict fragment size distribution of blasted rocks over an entire quarry. By comparing various prediction models, it can be ascertained that the result obtained from Kuz-Ram model relatively coincides with that of field measurements. Kuz-Ram model uses the concept of rock factor to signify conditions of rock mass such as block size, rock jointing, strength and others. For the evaluation of total production cost, it is imperative to estimate 3-D spatial distribution of rock factor for the entire quarry. In this study, a sequential indicator simulation technique is adopted for estimation of spatial distribution of rock factor due to its higher reproducibility of spatial variability and distribution models than Kriging methods. Further, this can reduce the uncertainty of predictor using distribution information of sample data. The entire quarry is classified into three types of rock mass and optimum blasting pattern is proposed for each type based on 3-D spatial distribution of rock factor. In addition, plane maps of rock factor distribution for each ground level are provided to estimate production costs for each process and to make a plan for an optimum blasting pattern.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.11
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• pp.600-605
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• 2013

Ground source heat pump systems (GSHP) can achieve higher performance of the system, by supplying more efficient heat source to the heat pump, than the conventional air-source heat pump system. But building clients and designers have hesitated to use GSHP systems, due to expensive initial cost, and uncertain economic feasibility. In order to reduce the initial cost, many researches have focused on the energy-pile system, using the structure of the building as a heat exchanger. Even though several experimental studies for the energy-pile system have been conducted, there was not enough data of quantitative evaluation with economic analysis and comprehensive analysis for the energy-pile. In this study, a prediction method has been developed for the energy pile system with barrette pile, using the ground heat transfer model and ground heat exchanger model. Moreover, a feasibility study for the energy pile system with barrette pile was conducted, by performance analysis and LCC assessment. As a result, it was found that the heat exchange rate of a barrette pile was 2.55 kW, and the payback period using LCC analysis was 8.8 years.

• Proceedings of the ESK Conference
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• 1994.04a
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• pp.115-123
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• 1994

A man model can be used as an effective tool to design ergomonically sound products and workplaces, and subsequently evaluate them properly. For a man model to be truly useful, it must be integrated with a posture prediction model which should be capable of representing the human arm reach posture in the context of equipments and workspaces. Since the human movement possesses redundant degrees of freedom, accurate representation or prediction of human movemtn was known to be a difficult problem. To solve this redundancy problem, the psychophysical cost function can predict the arm reach posture accurately. But the joint discomfort that human feels at the joint can not be predicted since the effects of external factors on the joint discomfort is not known. In this study a psychophysical experi- ment using the magnitude estimation technique was performed to evaluate the effects of external factors such as joint, joint angle and Perceived Exertion Ratio on the joint discomfort. Results showed that the joint discomfort increased as the Perceived Exertion Ratio increased, but the relation is not linear and was affected not only by the joint but also by the joint angle for the same Perceived Exertion Ratio. The interaction effect of the joint and the joint angle was also significant. From the results it is needed to develope the cost function which can predict the joint discomfort considering the joint, joint angle and external load.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.603-615
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• 2007

Software changes for various kinds of reasons and they increase maintenance cost. Software metrics, as quantitative values about attributes of software, have been adopted for predicting maintenance cost and fault-proneness. This paper proposes relationship between some typical object-oriented metrics and software changes in industrial settings. We used seven metrics which are concerned with size, complexity coupling, inheritance and polymorphism, and collected data about the number of changes during the development of an Information system on .NET platform. Based on them, this paper proposes a model for predicting the number of changes from the object-oriented metrics using multiple regression analysis technique.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Journal of the Korean Society for Railway
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• v.13 no.1
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• pp.71-77
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• 2010

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.

• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.1923-1931
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• 2004

Manufacturing of complex surface plates in stern and stem is a major factor in cost of a preliminary ship design by computing process. If these hull plate parts are effectively classified, it helps to compute the processing cost and find the way to cut-down the processing cost. This paper presents a new method to classify surface plates effectively in the preliminary ship design using neural network. A neural-network-based ship hull plate classification program was developed and tested for the automatic classification of ship design. The input variables are regarded as Gaussian curvature distributions on the plate. Various applicable rules of network topology are applied in the ship design. In automation of hull plate classification, two different numbers of input variables are used. By observing the results of the proposed method, the effectiveness of the proposed method is discussed. As a result, high prediction rate was achieved in the ship design. Accordingly, to the initial design stage, the ship hull plate classification program can be used to predict the ship production cost. And the proposed method will contribute to reduce the production cost of ship.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.252-260
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• 2004

Recently, the TPR-tree has been proposed to support spatio-temporal queries for moving objects. Subsequently, various methods using the TPR-tree have been intensively studied. However, although the TPR-tree is one of the most popular access methods in spatio-temporal databases, any cost model for the TPR-tree has not yet been proposed. Existing cost models for the spatial index such as the R-tree do not accurately ostinato the number of disk accesses for spatio-temporal queries using the TPR-tree, because they do not consider the future locations of moving objects. In this paper, we propose a cost model of the TPR-tree for moving objects for the first time. Extensive experimental results show that our proposed method accurately estimates the number of disk accesses over various spatio-temporal queries.