• International conference on construction engineering and project management
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• 2015.10a
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• pp.631-635
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• 2015

Construction managers use scheduling methods to improve the outcome of their project. In spite of the many obvious advantages of the critical path method (CPM), its use in construction has been limited. Understanding the reasons why CPM is not used as extensively as expected could improve its level of acceptance in the construction industry. The link between construction scheduling methods and the capabilities of the scheduling staff has been an on-going concern in the construction industry. This study proposes a staffing-technology fit model to understand why CPM is not used as extensively as expected in construction scheduling. A staffing-technology fit model that aims to measure the extent to which a construction scheduling method matches the staff's experience, know-how and capabilities. The model that is proposed is an answer to the lack of proper instruments for evaluating the extent to which scheduling methods are used in the industry.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.65.3-66
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• 2015

The determination of three dimensional parameters (e.g., radial speed, angular width, source location) of Coronal Mass Ejections (CMEs) is very important for space weather forecast. To estimate these parameters, several cone models based on a flat cone or a shallow ice-cream cone with spherical front have been suggested. In this study, we investigate which cone model is proper for halo CME morphology using 33 CMEs which are identified as halo CMEs by one spacecraft (SOHO or STEREO-A or B) and as limb CMEs by the other ones. From geometrical parameters of these CMEs such as their front curvature, we find that near full ice-cream cone CMEs (28 events) are dominant over shallow ice-cream cone CMEs (5 events). So we develop a new full ice-cream cone model by assuming that a full ice-cream cone consists of many flat cones with different heights and angular widths. This model is carried out by the following steps: (1) construct a cone for given height and angular width, (2) project the cone onto the sky plane, (3) select points comprising the outer boundary, (4) minimize the difference between the estimated projection points with the observed ones. We apply this model to several halo CMEs and compare the results with those from other methods such as a Graduated Cylindrical Shell model and a geometrical triangulation method.

• Journal of Positioning, Navigation, and Timing
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• v.1 no.1
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• pp.51-57
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• 2012

In this paper, design of an error model is presented in which the bias characteristic of the MEMS IMU is taken into consideration for performance enhancement of the MEMS IMU-based GPS/INS integrated navigation system. The drift bias of the MEMS IMU is modeled as a 1st-order Gauss-Markov (GM) process, and the autocorrelation function is obtained from the collected IMU data, and the correlation time is estimated from this. Prior to obtaining the autocorrelation function, the noise of IMU data is eliminated based on wavelet. As a result of simulation, it is represented that the parameters of error model can be estimated correctly only when a proper denoising is performed according to dynamic behavior of drift bias, and that the integrated navigation system based on error model, in which the drift bias is considered, provides more correct navigation performance compared to the integrated navigation system based on error model in which the drift bias is not considered.

• Journal of Ocean Engineering and Technology
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• v.22 no.5
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• pp.61-68
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• 2008

In Jinhae-Masan bay, a typical semi-dosed bay in Korea, the water quality is severely deteriorated because of the dosed topographic character and the inflow of nutrients from the land. There have been attempts to apply a water quality model dealing with the entrophication phenomenon and the oxygen-deficient mass in the bay in summer, but there have been few examples of models that have considered the phenomenon of stratification in the proper order, and then it is performed the model of water quality. Therefore, this study collected and analyzed the pre-observed water temperature data from Jinhae-Masan bay in summer and then constructed a density model using the topographic heat accumulation effect and inflow from the river to examine the temperature stratification. The simulation results show that this model could demonstrate the temperature stratification in the Jinhae-Masan bay very well.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.2
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• pp.97-103
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• 1997

DO concentration in the aquatic system is important for the water quality management perspective. Water quality model uses available reaeration coefficient (K2) estimation equations in calculating DO, however, they might include inevitable uncertainty that the model output can be less reliable. In this study, the calibrated QUAL2E model for the Passaic River in New Jersey, U.S., was used to examine the effect of K2 estimation equation on the output DO concentration of the river. The model was run with six commonly used equations separately with all the other conditions remained same. The result showed that the output DO concentration profiles varied widely with different equations, and maximum difference was 4.96 mg/L for the same location which is unacceptably large. It implies that the development of reliable equation is required for proper water quality management. The unreliable model output can lead to a wrong decision in water quality management such as unnecessarily high or too low treatment of wastewater, which will cause serious effect on the community economically and socially in either case. Generating more reliable model output with slight investment to develop a site specific K$_2$ equation can improve the decision making process significantly and is highly recommended.

• Steel and Composite Structures
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• v.15 no.1
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• pp.81-101
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• 2013

In this paper a robust 2-noded connection element has been developed for modelling the bolted end-plate connection between steel beam and column at elevated temperatures. The numerical procedure described is based on the model proposed by Huang (2011), incorporating additional developments to more precisely determinate the tension, compression and bending moment capacities of end-plate connection in fire. The proper failure criteria are proposed to calculate the tension capacity for each individual bolt row. In this new model the connection failure due to bending, axial tension, compression and shear are considered. The influence of the axial force of the connected beam on the connection is also taken into account. This new model has the advantages of both the simple and component-based models. In order to validate the model a total of 22 tests are used. It is evident that this new connection model has ability to accurately predict the behaviour of the end-plate connection at elevated temperatures, and can be used to represent the end-plate connections in supporting performance-based fire resistance design of steel-framed composite buildings.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.3-12
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• 2001

It is known that the roughness is one of the most important metrics in assessing the sound quality. In this study, a new roughness model is suggested by combing the previous auditory filter model and several signal processing methods for the enhancement of calculation efficiency and accuracy. For testing the usefulness of the present model, the predicted responses are compared with the experimental data and it is observed that they are in good agreements. Also, it is found that the previous models have limitations to search frequency components mainly contributed to overall roughness. By modifying the correlation criteria of the present model, the revised model for the proper estimation of roughness-contributed components is embedded.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.2
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• pp.95-101
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• 2018

Diagonally reinforced concrete coupling beams (DRCB) play an important role in coupled shear wall systems since these elements dissipate most of seismic input energy under earthquake loading. For reliable seismic performance evaluation using nonlinear response history analysis, it is important to use an accurate analytical model for DRCBs. In this study, the Pinching4 model is used as a base model to simulate the cyclic behavior of DRCBs. For simulating the cyclic behavior of DRCBs using the Pinching4 model, the analytical parameters for backbone curve, pinching and cyclic deterioration in strength and stiffness should be computed. To determine the proper values of the constituent analytical parameters efficiently and accurately, this study proposes the empirical equations for the analytical parameters using regression analyses. It is shown that the hysteretic behavior of coupling beams can be simulated efficiently and accurately using the proposed numerical model with the proposed empirical equations of model parameters.

• Journal of Information Technology Applications and Management
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• v.11 no.1
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• pp.117-135
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• 2004

Since human decision making behavior is likely to follow nonlinear strategy, it is conjectured that the human decision making behavior can be modeled better by nonlinear models than by linear models. All that linear models can do is to approximate rather than model the decision behavior. This study attempts to test this conjecture by analyzing human decision making behavior and combining the results of the analysis with predictive performance of both linear models and nonlinear models. In this way, this study can examine the relationship between the predictive performance of models and the existence of valid nonlinear strategy in decision making behavior. This study finds that the existence of nonlinear strategy in decision making behavior is highly correlated with the validity of the decision (or the human experts). The second finding concerns the significant correlations between the model performance and the existence of valid nonlinear strategy which is detected by Lens Model. The third finding is that as stronger the valid nonlinear strategy becomes, the better nonlinear models predict significantly than linear models. The results of this study bring an important concept, validity of nonlinear strategy, to modeling human experts. The inclusion of the concept indicates that the prior analysis of human judgement may lead to the selection of proper modeling algorithm. In addition, lens Model Analysis is proved to be useful in examining the valid nonlinearity in human decision behavior.