• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1566-1577
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• 2018

This paper presents a stability analysis of AC-DC power system feeding a speed controlled DC motor in which this load behaves as a constant power load (CPL). A CPL can significantly degrade power system stability margin. Hence, the stability analysis is very important. The DQ and generalized state-space averaging methods are used to derive the mathematical model suitable for stability issues. The paper analyzes the stability of power systems for both speed control natural frequency and DC-link parameter variations and takes into account controlled speed motor dynamics. However, accurate DC-link filter and DC motor parameters are very important for the stability study of practical systems. According to the measurement errors and a large variation in a DC-link capacitor value, the system identification is needed to provide the accurate parameters. Therefore, the paper also presents the identification of system parameters using the adaptive Tabu search technique. The stability margins can be then predicted via the eigenvalue theorem with the resulting dynamic model. The intensive time-domain simulations and experimental results are used to support the theoretical results.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.195-199
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• 2015

Many conventional management methods have emphasized the minimization of required resources along the supply chain. Accordingly, this paper presents a proposed method called the Virtual Design and Construction (VDC)-aided system. It is based on object-oriented resource control, in order to accomplish a feed-forward control monitoring supply chain logistics. The system is supported by two main parts: (1) IT-based Technologies; and (2) VDC Models. They enable the system to convey proactive information from the detection technology to its linked visualization. The paper includes a field study as the system's pre-test: the Scaffolding Works in a LNG Mega Project. The study demonstrates a system of real-time productivity monitoring by use of the RFIDbased Mobile Information Hub. The on-line 'productivity dashboard' provides an opportunity to display the continuing processes for each work-package. This research project offers the observed opportunities created by the developed system. Future work will entail research experiments aimed towards system validation.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.80-87
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• 2010

This study was conducted to develop a reservoir modelling workflow to reproduce the heterogeneous distribution of effective permeability that impacts on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique in the Athabasca Oil Sands. Lithologic facies distribution is the main cause of the heterogeneity in bitumen reservoirs in the study area. The target formation consists of sand with mudstone facies in a fluvial-to-estuary channel system, where the mudstone interrupts fluid flow and reduces effective permeability. In this study, the lithologic facies is classified into three classes having different characteristics of effective permeability, depending on the shapes of mudstones. The reservoir modelling workflow of this study consists of two main modules; facies modelling and permeability modelling. The facies modelling provides an identification of the three lithologic facies, using a stochastic approach, which mainly control the effective permeability. The permeability modelling populates mudstone volume fraction first, then transforms it into effective permeability. A series of flow simulations applied to mini-models of the lithologic facies obtains the transformation functions of the mudstone volume fraction into the effective permeability. Seismic data contribute to the facies modelling via providing prior probability of facies, which is incorporated in the facies models by geostatistical techniques. In particular, this study employs a probabilistic neural network utilising multiple seismic attributes in facies prediction that improves the prior probability of facies. The result of using the improved prior probability in facies modelling is compared to the conventional method using a single seismic attribute to demonstrate the improvement in the facies discrimination. Using P-wave velocity in combination with density in the multiple seismic attributes is the essence of the improved facies discrimination. This paper also discusses sand matrix porosity that makes P-wave velocity differ between the different facies in the study area, where the sand matrix porosity is uniquely evaluated using log-derived porosity, P-wave velocity and photographically-predicted mudstone volume.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.422-432
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• 2013

A System Identification method to develop parametric models linking free surface elevation and wave pressure is presented and two models are built allowing for either wave pressure or free surface elevation simulation. Linear, time invariant model structures with static nonlinearities are assumed and solutions are sought in a form of autoregressive model with extra input (ARX). An arbitrary chosen free-surface elevation and wave pressure dataset is used for estimation of the models, which are subsequently verified against datasets with similar pressure gauge depth but different free-surface elevation spectra due to different meteorological conditions. It is shown that free-surface simulation using System Identification methods can perform better than traditional linear transfer function derived from linear wave theory (LTF), while wave pressure simulation quality using presented methods is generally similar to that obtained with corrected LTF.

• Smart Structures and Systems
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• v.33 no.2
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• pp.119-131
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• 2024

Drift angle is a significant index for diagnosing post-event structures. A common way to estimate this drift response is by using modal parameters identified under natural excitations. Although the modal parameters of shear structures cannot be identified accurately in the real environment, the identification error has little impact on the estimation when measurements from several floors are used. However, the estimation accuracy falls dramatically when there is only one accelerometer. This paper describes the susceptibility of single sensor identification to modelling error and simulations that preliminarily verified this characteristic. To make a robust evaluation from measurements of one floor of shear structures based on imprecisely identified parameters, a novel scheme is devised to approximately correct the mode shapes with respect to fictitious frequencies generated with a genetic algorithm; in particular, the scheme uses constrained minimization to take both the mathematical aspect and the realistic aspect of the mode shapes into account. The algorithm was validated by using a full-scale shear building. The differences between single-sensor and multiple-sensor estimations were analyzed. It was found that, as the number of accelerometers decreases, the error rises due to insufficient data and becomes very high when there is only one sensor. Moreover, when measurements for only one floor are available, the proposed method yields more precise and appropriate mode shapes, leading to a better estimation on the drift angle of the lower floors compared with a method designed for multiple sensors. As well, it is shown that the reduction in space complexity is offset by increasing the computation complexity.

• Journal of Biosystems Engineering
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• v.36 no.6
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• pp.476-483
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• 2011

The aerial application using an unmanned helicopter has been already utilized and an attitude controller would be developed to enhance the operational convenience and safety of the operator. For a preliminary study of designing flight controller, a state space model for an RC helicopter would be identified. Frequency sweep flight tests were performed and time history data were acquired in the previous study. In this study, frequency response of the flight test data of a small unmanned helicopter was analyzed by using the CIFER software. The time history flight data consisted of three replications each for collective pitch, aileron, elevator and rudder sweep inputs. A total of 36 frequency responses were obtained for the four control stick inputs and nine outputs including linear velocities and accelerations and angular velocities in 3-axis. The results showed coherence values higher than 0.6 for every primary control inputs and corresponding on-axis outputs for the frequency range from 0.07 to 4 Hz. Also the analysis of conditioned frequency response showed its effectiveness in evaluating cross coupling effects. Based on the results, the dynamic characteristics of the model helicopter can further be analyzed in terms of transfer functions and the undamped natural frequency and damping ratio of each critical mode.

• Steel and Composite Structures
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• v.20 no.3
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• pp.719-729
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• 2016

There are great needs of simple but reliable mechanical nonlinear behavior analysis and performance evaluation method for frames constructed by steel and concrete composite beams or columns when the structures subjected extreme loads, such as earthquake loads. This paper describes an approach of simplified macro-modelling for composite frames consisting of steel-concrete composite beams and CFST columns, and presents the performance evaluation procedure based on the pushover nonlinear analysis results. A four-story two-bay composite frame underground is selected as a study case. The establishment of the macro-model of the composite frame is guided by the characterization of nonlinear behaviors of composite structural members. Pushover analysis is conducted to obtain the lateral force versus top displacement curve of the overall structure. The identification method of damage degree of composite frames has been proposed. The damage evolution and development of this composite frame in case study has been analyzed. The failure mode of this composite frame is estimated as that the bottom CFST columns damage substantially resulting in the failure of the bottom story. Finally, the seismic performance of the composite frame with high strength steel is analyzed and compared with the frame with ordinary strength steel, and the result shows that the employment of high strength steel in the steel tube of CFST columns and steel beam of composite beams benefits the lateral resistance and elasticity resuming performance of composite frames.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.103-113
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• 2020

The construction industry is undergoing a digital transformation in which Building Information Modelling (BIM) is a key technology. The potential of BIM in several areas such as design optimization, time management, cost management, and asset management/facility management (AM/FM) is widely acknowledged by the AECO (Architecture, Engineering, Construction, and Operation) industry around the world. However, BIM implementation in construction projects is faced with problems such as project delay and cost overruns. The lack of identification of risks in BIM projects and standard guidelines on mitigation techniques furthers poor performance, dissatisfaction, and disputes between employers and project participants, which results in low BIM adoption rates. Therefore, the objective of this paper is to identify the potential risks in BIM implementation under the primary categories - (1) technical, (2) contractual, (3) management-related, and (4) personnel-related risks in BIM projects and present solutions to reduce, manage, and mitigate risks. To meet the objective of this paper, a survey was designed and conducted in the Hong Kong construction industry in which over 140 respondents from different disciplines, with experience in BIM projects, have participated. Based on the analysis of the survey data, the most severe and frequently occurring BIM risks and their potential mitigation strategies were identified and discussed in this paper.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.221-225
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• 2004

This paper deals with a nonlinear system modelling using neural network and genetic algorithm Application q{ neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. in this paper, we optimize a neural network structure using genetic algorithm The genetic algorithm uses binary coding for neural network structure and searches for an optimal neural network structure of minimum error and fast response time. Through an extensive simulation, the optimal neural network structure is shown to be effective for identification of nonlinear system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.2
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• pp.18-26
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• 1992

MR(Model Reduction) is presented in order to estimate the frequency of multiple sinusoids from the finite noisy data with the white or colored noises. MR, using the reduced rank models, is designed, appling the approximation of linear system to LP(Linear Prediction). The MR method is analyzed. Monte-carlo simulations are conducted for MR and Lp. The results are compared with in terms of mean, root-mean square and relative bias. MR eliminates effectevely the extremeous and exceptional poles appearing in LP and improves the accuracy of LP. Especially, MR gives promising results in short noisy measurements, low SNR's and colored noises. Power spectral density and angular frequency position are showed by figures, for examples. Finally, the new method is utilized to the communication and biomedical systems estimating the characteristics of the signal and the system identification modelling the dynamic systems from experimental data.