• Journal of the Korea Institute of Building Construction
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• v.23 no.4
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• pp.497-508
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• 2023

Manual estimation of rebar quantities by contractors often yields discrepancies between projected and actual amounts used in the construction phase, leading to cost inaccuracies and potential logistical challenges. To address these issues, there is a clear need for a method that allows precise estimation of rebar quantities during the design phase. Such a method would enhance contractor competitiveness during project bids, promote accurate cost calculations, and avert superfluous rebar purchases on-site. Given that columns are the primary structural components in reinforced concrete(RC) buildings and necessitate considerable amounts of rebar, this study focuses on creating an automated algorithm for estimating column rebar quantities. An analysis of the accurate quantities obtained via the study and those derived from manual estimation reveals a discrepancy of 0.346 tons or 2.056%. This comparison affirms the proposed algorithm's efficacy in eliminating errors from overestimation or underestimation of rebar quantities. The practical implications of this study are significant for construction companies as it fosters efficient and precise estimation of rebar quantities, ensuring compliance with related specifications and governing regulations.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1491-1495
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• 2023

This study utilized automated machine learning (AutoML) to calculate Arctic ice surface temperature (IST). AutoML-derived IST exhibited a strong correlation coefficient (R) of 0.97 and a root mean squared error (RMSE) of 2.51K. Comparative analysis with deep neural network (DNN) models revealed that AutoML IST demonstrated good accuracy, particularly when compared to Moderate Resolution Imaging Spectroradiometer (MODIS) IST and ice mass balance (IMB) buoy IST. These findings underscore the effectiveness of AutoML in enhancing IST estimation accuracy under challenging polar conditions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.2
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• pp.107-113
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• 2013

When the main spindle of high precision machine tools are run many hours, heat is generated in bearing parts of the inside of the spindle. Also, headstock is appeared distortion by inside and outside temperature difference of a machine. This paper studies method to measure behavior of machine tool about these thermal effects. In addition, it estimates measurement uncertainty factors which can be appeared in thermal effects measurement. Finding the factor of thermal affect measurement is important for estimation of measurement uncertainty. This paper measures thermal effects of high precision machine tools and evaluates the important factors of uncertainty.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.169-175
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• 2001

Most of studies on automated storage/retrieval (AS/R) system assumed that storage capacity is given, although it is a very important decision variable in the design phase. We propose a simple algorithm to estimate the required storage capacity, i.e., number of aisles and number of openings in vertical and horizontal directions in each aisle, of an AS/R system under stochastic demand, in which storage requests occur endogenously and exogenously while the retrieval requests occur endogenously from the machines. Two design criteria, maximum permissible overflow probability and maximum allowable storage/retrieval (S/R) machine utilization, are used to compute the storage capacity. This model can be effectively used in the design phase of new AS/R systems.

• Journal of the Korea Society for Simulation
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• v.10 no.3
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• pp.71-82
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• 2001

Considering material handling delay which occurs between storage and processing stations, we propose an algorithm to estimate the required storage capacity, i.e., number of aisles and number of openings in vertical and horizontal directions in each aisle, of an automated storage/retrieval(AS/R) system. Due to the random nature of storage and retrieval requests, proportion of single and dual commands is not known in advance. Two design criteria, maximum permissible overflow probability and maximum allowable storage/retrieval(S/R) machine utilization, are used to compute the storage capacity. Most of studies assume that storage capacity of AS/R systems is given, although it is a very important decision variable in the design phase. Therefore, the proposed model can be effectively used in the design phase of new AS/R systems.

• Smart Media Journal
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• v.8 no.4
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• pp.72-79
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• 2019

The automated demand response (DR) program encourages consumers to participate in grid operation by reducing power consumption or deferring electricity usage at peak time automatically. However, successful deployment of the automated DR program sphere needs careful assessment of appliances load profile (ALP). To this end, the recent method estimates frequency, consistency, and peak time consumption parameters of the daily ALP to compute their potential score to be involved in the DR event. Nonetheless, as the daily ALP is subject to varying with respect to the DR time ALP, the existing method could lead to an inappropriate estimation; in such a case, inappropriate appliances would be selected at the automated DR operation that effected a consumer comfort level. To address this challenge, we propose a more proper method, in which all the three parameters are calculated using ALP that overlaps with DR time, not the total daily profile. Furthermore, evaluation of our method using two public residential electricity consumption data sets, i.e., REDD and REFIT, shows that our energy management systems (EMS) could properly match a DR target. A more optimal selection of appliances for the DR event achieves a power consumption decreasing target with minimum comfort level reduction. We believe that our approach could prevent the loss of both utility and consumers. It helps the successful automated DR deployment by maintaining the consumers' willingness to participate in the program.

• Journal of Sensor Science and Technology
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• v.9 no.5
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• pp.373-379
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• 2000

In this paper, an estimation system of vehicle position and orientation on magnetic lane, which is a parameter of the steering controller for automated lane following is described. To verify that the magnetic dipole model could be applied to a magnetic unit paved in roadway, the analysis of the model is compared with the data of 3-axis magnetic field measured experimentally. The sensor location could be estimated by analysis of the model based on experimental data. For the magnetic lane model merged magnetic unit, the relation of sensor location and magnetic field is acquired experimentally. The proposed estimation of vehicle position and orientation is adopted to automated lane following by computer simulation.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.77-86
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• 1989

A fully automated cost estimation system(FACES) has been developed. Since speed, accuracy, and consistency are essential factors in automating a cost estimation, the use of computers in cost estimation system(CES) has grown rapidly in the last few years. FACES is a micro computer based cost estimation system that employs a manufacturing knowledge base. A Group Technology(GT) based part classification and coding(C&C) scheme is used to automate the process planning aspects of cost estimation. Variant process planning methods are employed to generate workstation routings from form features of the part. The system has been tested for an assembly of six machined parts. Results indicate that the system could provide a substantial improvement in accuracy, productivity, and performance over the more traditional full dialog approach to cost estimation. It also provides a good foundation for a factory automation by using a common GT based database through design to production.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.16-20
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• 2022

Accurate state estimation is important for autonomous driving. However, the estimation error increases in situations that a lot of longitudinal slip occurs. Therefore, this paper presents a vehicle state estimation method using an Extended Kalman Filter. The filter estimates the states of the host vehicle robust to wheel slip. It utilizes the measurements of the four-wheel rotational speeds, longitudinal acceleration, yaw-rate, and steering wheel angle. Nonlinear measurement model is represented by Ackermann Model. The main advantage of this approach is the accurate estimation of yaw rate due to the measurement of the steering wheel angle. The proposed algorithm is verified in scenarios of autonomous emergency braking (AEB), lane change (LC), lane keeping (LK) using an automated vehicle. The results show that the proposed algorithm guarantees accurate estimation in such scenarios.

• Journal of Korean Society of societal Security
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• v.1 no.2
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• pp.73-78
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• 2008

Recently, large scale natural disasters such as floods and typhoons due to climate change have been occurring all over the world causing severe damages. Among the various efforts to reduce and recover damages, recently, advanced information technology and remote sensing techniques are applied in disaster management. In this study, a real-time automated damage estimation system using information technology and spatial imagery was developed to accomplish prompt and accurate disaster damage estimation. This system is able to estimate the damage amounts of public facilities such as roads, rivers, bridges automatically through spatial imageries including ground based digital images, aerial photos, satellite images of disaster sites. Based on these spatial imageries, the damage amounts are analyzed in the Web-GIS based analysis system. Consequently, the digital damage reports such as digital disaster information sheets and damage maps can be made promptly and accurately. This system can be a useful tool to carry out prompt disaster damage estimation and efficient disaster recovery.