• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.111-112
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• 2012

Korean government is planning to supply a half million public and permanent rental houses from 2013 to 2018 for settlement of non-homeowners. It is requested an objective criterion to appropriate the budget for the rental houses construction project cost. In this study, construction cost indices, which reflect the inflation trend of construction resources, were explorated to suggest a effective methodology for the construction cost estimation of therental houses. We figured out the future construction cost indices using several scientific methods, and seven estimated indices values were shown. It is required an additional research to select the proper value among the analyzed indices.

• Atmosphere
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• v.15 no.1
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• pp.35-46
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• 2005

Today, the outreach of National Meteorological Service such as PC cluster based Numerical Weather Prediction (NWP) technique is vigorous in the world wide. In this regard, WMO (World Meteorological Organization) asked KMA (Korea Meteorological Administration) to formulate a regional project, which cover most of RA II members, using similar technical system with KMA's. In that sense, Meteorological Research Institute (METRI) in KMA developed Mongolian NWP System (MNWPS) based on PC cluster and transferred the technology to Weather Service Center in Mongolia. The hybrid parallel algorithm and channel bonding technique were adopted to cut cost and showed 41% faster performance than single MPI (Message Passing Interface) approach. The cluster technique of Beowulf type was also adopted for convenient management and saving resources. The Linux based free operating system provide very cost effective solution for operating multi-nodes. Additionally, the GNU software provide many tools, utilities and applications for construction and management of a cluster. A flash flood event happened in Mongolia (2 September 2003) was selected for test run, and MNWPS successfully simulated the event with initial and boundary condition from Global Data Assimilation and Prediction System (GDAPS) of KMA. Now, the cluster based NWP System in Mongolia has been operated for local prediction around the region and provided various auxiliary charts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4087-4107
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• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.872-883
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• 2011

SVC is an emerging video coding standard as an extension of H.264/AVC. This standard uses inter prediction, intra prediction and a new inter-layer prediction to improve coding performance of enhancement layers. However, it has high computational complexity. In this paper, we propose an efficient intra prediction mode decision method in the spatial enhancement layer to reduce the computational complexity. The proposed method consists of two phases. In the first phase, Intra_BL mode is selected using the RD cost of Intra_BL in advance. We exploit the fact that the RD cost and prediction mode are similar to those of neighbor macroblocks. In the second phase, we predict the enhancement layer mode using correlation between intra mode of enhancement layer and that of the base layer. Experimental results show that the proposed method could save from 48.15% to 56.32% in encoding time while degradation in video quality is negligible.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.41-48
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• 2012

Red tide is a natural phenomenon temporary blooming harmful algal with changing sea color from normal to red, which fish and shellfish die en masse. It also give a bad influence to coastal environment and sea ecosystem. The damage of sea farming by a red tide has been occurred each year which it cost much to prevent disasters of red tide blooms. Red tide damage and prevention cost of red tide disasters can be minimized by means of prediction of red tide blooms. In this paper, we proposed the red tide blooms prediction method using ensemble train. The proposed method use the bagging and boosting ensemble train methods for enhancing red tide prediction and forecast. The experimental results demonstrate that the proposed method achieves a better red tide prediction performance than other single classifiers.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1037-1039
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• 2002

가전산업과 연계하여 유도가열 기술을 응용한 대표적인 시스템으로 IH압력밥솥을 예로 들 수 있다. 유도가열 코일에서 고주파 자속의 의해 발생된 와전류가 결합된 밥솥을 가열하기 때문에 유도가열 코일에는 고주파 손실을 줄이기 위해 Litz wire가 사용된다 이에 본 논문은 특히 IH압력밥솥에 사용되는 Litz wire를 선정하는 데 있어 중요한 요소가 되는 소선경, 가닥수별 AC DC 저항 및 손실계산 그리고 Cost 예측 방법을 제안한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1299-1306
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• 2014

Recently, there are many roundabouts installation works which are ordered to provide convenient transportation to road users as well as to eliminate traffic accidents and traffic delays. This study propose an approximate construction cost estimation model for early stages of roundabout construction. The model is designed considering the conditions of the early stage roundabout construction sites in Jeju. The regression equation of approximate construction cost was derived through regression analysis of 25 design data of roundabout construction in Jeju, and it was analyzed to have a high prediction accuracy. Finally, results verifies high prediction accuracy of the derived regression equation. Difference between the estimation cost and the design cost was only 2.3%, 3.7%, and 5.8% that verifies the high accuracy of the proposed approximate construction cost estimation model.

• Journal of the Korean Institute of Educational Facilities
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• v.25 no.6
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• pp.35-44
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• 2018

The purpose of this study is to predict more accurate construction costs and to support efficient decision making in the planning and design stages of smart education facilities. The higher the error in the projected cost, the more risk a project manager takes. If the manager can predict a more accurate construction cost in the early stages of a project, he/she can secure a decision period and support a more rational decision. During the planning and design stages, there is a limited amount of variables that can be selected for the estimating model. Moreover, since the number of completed smart schools is limited, there is little data. In this study, various artificial intelligence models were used to accurately predict the construction cost in the planning and design phase with limited variables and lack of performance data. A theoretical study on an artificial neural network and deep learning was carried out. As the artificial neural network has frequent problems of overfitting, it is found that there is a problem in practical application. In order to overcome the problem, this study suggests that the improved models of Deep Neural Network and Deep Belief Network are more effective in making accurate predictions. Deep Neural Network (DNN) and Deep Belief Network (DBN) models were constructed for the prediction of construction cost. Average Error Rate and Root Mean Square Error (RMSE) were calculated to compare the error and accuracy of those models. This study proposes a cost prediction model that can be used practically in the planning and design stages.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2676-2685
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• 1998

In order to improve the branch prediction accuracy and to reduce the BTB miss rate, this paper proposes a two-level BTB structure that adds small-sized victim BTB to the convetional BTB. With small cost, two-level BTB can reduce the BTB miss rate as well as improve the prediction accuracy of the hybrid branch prediction strategy which combines dynamic prediction and static prediction. Through the trace-driven simulation of four bechmark programs, the performance improvement by the proposed two-level BTB structure is analysed and validated. Our proposed BTB structure can improve the BTB miss rate by 26.5% and the misprediction rate by 26.75%