• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.36-39
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• 1996

In chemical processes, measurement errors reduce the credibility of information and cause inconsistency in material and energy balances. Because multicomposition flows and temperature measurements make material and energy balances nonlinear equations, data reconciliation becomes a nonlinear constrained optimization problem. In multicomposition processes, if we follow general optimization procedure, the number of measurement variables is so large that data reconciliation requires much computation time. We propose the decomposition procedure to reduce the computation time without the decrease of accuracy of data reconciliation. Decomposition procedure finds global variables, that can reduce the nonlinearity of constraints, and divides two sub-optimization problems. Once we optimize the global variables at upper level, we can easily optimize the remain variables at tower level, We can obtain the short computational time and the same accuracy as SQP optimization method.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1841-1851
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• 2018

For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.23 no.4
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• pp.301-327
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• 2019

The proper orthogonal decomposition (POD) method for time-dependent problems significantly reduces the computational time as it reduces the original problem to the lower dimensional space. Even a higher degree of reduction can be reached if the solution is smooth in space and time. However, if the solution is discontinuous and the discontinuity is parameterized e.g. with time, the POD approximations are not accurate in the reduced space due to the lack of ability to represent the discontinuous solution as a finite linear combination of smooth bases. In this paper, we propose to post-process the sample solutions and re-initialize the POD approximations to deal with discontinuous solutions and provide accurate approximations while the computational time is reduced. For the post-processing, we use the Gegenbauer reconstruction method. Then we regularize the Gegenbauer reconstruction for the construction of POD bases. With the constructed POD bases, we solve the given PDE in the reduced space. For the POD approximation, we re-initialize the POD solution so that the post-processed sample solution is used as the initial condition at each sampling time. As a proof-of-concept, we solve both one-dimensional linear and nonlinear hyperbolic problems. The numerical results show that the proposed method is efficient and accurate.

• Korean Journal of Environment and Ecology
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• v.25 no.6
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• pp.887-892
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• 2011

This study was to find out influence of heavy metal concentration in plant on microbial activities during decomposition of Artenmisia princeps var. orientalis and Equisetum arvense collected from an abandoned mine and control site in Cheongyang-gun Chungcheongnam-do. Microbial respiration rate showed the highest value at the time of the first collection, and then tended to decline over time. The highest microbial respiration rate appeared in leaf litters with low heavy metal contents, and A. princeps var. orientalis and E. arvense collected and decomposed at the control site showed the fastest decomposition rate. For both A. princeps var. orientalis and E. arvense, litters with low heavy metal content appeared to have higher microbial biomass. There was apparent quantitative correlation between decomposition rate and cumulative respiration rate of leaf litters, and between decomposition rate and microbial biomass of leaf litters. Thus, the study result showed that leaf litter with higher heavy metal content had a negative impacts on the growth and activity of microbial decomposer during decomposition processes.

• International Journal of Automotive Technology
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• v.2 no.3
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• pp.117-122
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• 2001

For a dynamic analysis of a constrained multibody system, it is necessary to have a routine for satisfying kinematic constraints. LU decomposition scheme, which is used to divide coordinates into dependent and independent coordinates, is efficient but has great difficulty near the singular configuration. Other method such as the projection matrix, which is more stable near a singular configuration, takes longer simulation time due to the large amount of calculation for decomposition. In this paper, the row space and the null space of the Jacobian matrix are proposed by using the pseudo-inverse method and the projection matrix. The equations of the motion of a system are replaced with independent acceleration components using the null space of the Jacobian matrix. Also a new hybrid method is proposed, combining the LU decomposition and the projection matrix. The proposed hybrid method has following advantages. (1) The simulation efficiency is preserved by the LU method during the simulation. (2) The accuracy of the solution is also achieved by the projection method near the singular configuration.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.2
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• pp.117-127
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• 2000

In this paper, we propose a new convex combination weight rule for the cross decomposition method which is known to be one of the most reliable and promising strategies for the large scale optimization problems. It is called generalized cross decomposition, a modification of linear mean value cross decomposition for specially structured linear programming problems. This scheme puts more weights on the recent subproblem solutions other than the average. With this strategy, we are having more room for selecting convex combination weights depending on the problem structure and the convergence behavior, and then, we may choose a rule for either faster convergence for getting quick bounds or more accurate solution. Also, we can improve the slow end-tail behavior by using some combined rules. Also, we provide some computational test results that show the superiority of this strategy to the mean value cross decomposition in computational time and the quality of bounds.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.818-823
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• 2000

In practical design studies, most of designers solve multidisciplinary problems with complex design structure. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder original design processes to minimize total cost and time. This is accomplished by decomposing large multidisciplinary problem into several multidisciplinary analysis subsystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and multidisciplinary design optimization (MDO) methodology.

• Bulletin of the Korean Chemical Society
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• v.11 no.4
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• pp.343-347
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• 1990

A thermal decomposition of the tert-butoxy radical has been studied in the gas phase over the pressure range of 1-200 torr at $413_{\circ}K$ using di-tert-butyl peroxide + trimethylsilane mixtures. The relative rate constants were obtained by studying the competitive reactions between tert-butoxy radical decomposition 4(t-BuO·→ CH_3COCH_3 + CH_3·)$ and hydrogen abstraction reaction from trimethylsilane $(t-BuO·+ HSi(CH_3)_3 → t-BuOH + Si(CH_3)_3)·).$ The conventional RRKM calculations were carried out to compare the observed fall-off behavior of the decomposition rate constant $({\kappa}_d)$ with the theoretical predictions using reasonable values of input parameters. In all cases the calculated half-rate pressure $(P_{1/2})$ were significantly higher than those observed. The failure of RRKM to reproduce the fall-off behavior led us to suggest that not all of vibrational modes contribute to excitation (leading to decomposition) on the same time scale.

• Journal of the Korean Society of Safety
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• v.14 no.1
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• pp.73-77
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• 1999

In this study, $SO_2$ and $NO_2$ reduction have been investigated by using coil type plasma reactor. The experiments have been carried out changing discharge power, gas flow rate frequency and electrode style to obtain the decomposition rate. Decomposition rates of $SO_2$ and $NO_2$ were obtained 20~98% at gas flow rate 100ml/min~1,000ml/min and discharge power 5~25w respectively. The energy efficiency is very good at the high frequency power. The decomposition rate of $SO_2$ for 5kHz power supply is only 90%, but for 10kHz power supply is very high, more than 98% for 15w. The decomposition rate is increasing according to the residence time or the power consumption of the discharge. About 15W discharge power for 17$cm^2$ reactor is necessary to obtain the decomposition rate of $SO_2$ and $NO_2$ of more than 85% or 98%. From these experiments, the consumption power of the decomposition rate of 98% in 300ppm $NO_2$ gas in nitrogen gas proved to be 18W and 300ppm $SO_2$ gas to be 15w.