• Journal of the Korean Society of Safety
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• v.38 no.1
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• pp.1-8
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• 2023

It is extremely important to design safety-guaranteed industrial processes because such process determine the ultimate outcomes of industrial activities, including worker safety. Application of artificial intelligence (AI) in industrial safety involves modeling industrial safety systems by using vast amounts of safety-related data, accident prediction, and accident prevention based on predictions. As a preliminary step toward realizing AI-based industrial safety in Korea, this study discusses neural network-based modeling of industrial safety systems. The input variables that are the most discriminatory relative to the output variables of industrial safety processes are selected using two information-theoretic measures, namely entropy and cross entropy. Normalized frequency and severity of industrial accidents are selected as the output variables. Our simulation results confirm the effectiveness of the proposed neural network model and, therefore, the feasibility of extending the model to include more input and output variables.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.389-405
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• 1995

Output processes emanating from exit arcs in a mulitple server open Jackson network with node i having $s_i$ servers are determined. Beutler and Melamed (1978) showed, for traffics on all exit arcs of single server open Jackson network in equilibrium, that the customer streams leaving any exit set are Poisson and that the collections over all nodes which yield the Poisson departure processes are mutually independent. In this paper we generalize the above results to multiple servers open Jackson network in equilibrium. While no weak limit result is possible under the equilibrium condition, nonetheless approximations to the distributions of maximum queue lengths for no feedback nodes in multiple servers open Jackson network are established.

• Bulletin of the Korean Chemical Society
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• v.10 no.3
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• pp.282-288
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• 1989

Theoretical analysis of DF-$CO_2$ transfer chemical laser is performed through simple kinetic model consisting of 30 chemical reactions. In this model, we calculate the power theoretically by solving the rate equations, which are related to the $D_2\;+\;F_2$ chain reaction and the DF-$CO_2$ resonance energy transfer, combined with both the gain processes and the stimulated emission processes. The calculated powers are verified with previously reported results in good agreements. The output energy rises linearly with the increase in pressure, and the duration time of output pulse show the inverse dependence on pressure. Through the detailed calculation of temperature and concentrations of reactants as a function of time, it is found that the deactivation processes of DF(v) can be neglected in low pressure, but they have to be considered in high pressure. From the parametric study for the variation on [$D_2]/[F_2$] and [$CO_2]/[D_2\;+\;F_2$] at several constant total pressure, the optimum lasing conditions are found to be in a range of 1/3 to 1 and 2 to 4, respectively.

• IE interfaces
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• v.11 no.3
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• pp.89-101
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• 1998

The randomness of the input variables in simulation experiments produce output responses which are also realizations of random variables. The random responses make necessary the use of statistical inferences to adequately describe the stochastic nature of the output. The analysis of the simulation output of non-terminating simulations is frequently complicated by the autocorrelation of the output data and the effect of the initial conditions that produces biased estimates. The regenerative method has been developed to deal with some of the problems created by the random nature of the simulation experiments. It provides a simple solution to some tactical problems and can produce valid statistical results. However, not all processes can he modeled using the regenerative method. Other processes modeled as regenerative may not return to a given demarcating state frequently enough to allow for adequate statistical analysis. This paper shows how the state transformation concept was successfully used in a queueing model and a job shop model. Although the first example can be analyzed using the regenerative method. it has the problem of too few recurrences under certain conditions. The second model has the problem of no recurrences. In both cases, the state transformation increase the frequency of the demarcating state. It was shown that time state transformations are regenerative and produce more cycles than the best typical discrete demarcating state in a given run length.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.934-938
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• 1989

This paper considers the model predictive control (MPC) problems in nonlinear processes or systems. The MPC method determines the control law such that the predicted output based on the identified process model is equal to the reference output which consists of both the process output at current time and the setting value called as the command generator. In this paper, the nonlinear MPC software for a chemical reactor is developed and analized from the point of view of practical applications.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.6
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• pp.971-974
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• 1987

Considering heat transfer and heat loss processes for the heat engine operating between two fixed heat reservoirs, it may be qualitatively explained that the maxima of power output and its efficiency depending upon operating conditions are extreme maxima. Furthermore, it is also found that from an economic point of view the above two extremes are related to extreme minima of plant cost per unit power output and operation cost per unit power output respectively, and the condition of minimum cost per unit power output exists between the extreme minimum conditions of plant cost per unit power output and that of operation cost per unit power output.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1762-1765
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• 1997

Some ill-conditioned processes are very sensitive to small element-wise uncertainties arising in classical element-by-element model identifications. For such processes, accurate identification of simgular values and right singular vectors are more important than theose of the elements themselves. Singular values and right singular vectors can be found by iteraive identification methods which implement the input and output transformations iteratively. Methods based on SVD decomposition, QR decomposition and LU decomposition are proposed and compared with the Kuong and Mac Gregor's method. Convergence proofs are given. These SVD and QR mehtods use normal matrices for the transformations which cannot be calculated analytically in general and so they are hoard to apply to dynamic processes, whereas the LU method used simple analyitc transformations and can be directly applied to dynamic processes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.57-65
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• 2011

Monitoring auto correlated processes is prevalent in recent manufacturing environments. As a proactive control for manufacturing processes is emphasized especially in the semiconductor industry, it is natural to monitor real-time status of equipment through sensor rather than resultant output status of the processes. Equipment's sensor data show various forms of correlation features. Among them, considerable amount of sensor data, statistically autocorrelated, is well represented by Box-Jenkins autoregressive moving average (ARMA) model. In this paper, we present a design method of statistical process control (SPC) used for monitoring processes represented by the ARMA model. The proposed method shows benefits in the power of detecting process changes, and considers robustness to ARMA modeling errors simultaneously. We prove benefits through Monte carlo simulation-based investigations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.299-302
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• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.1
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• pp.225-229
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• 1990

As a basic study of optimal design conditions of refrigeration systems, the reversed carnot cycle, including heat transfer processes through the finite temperature differences between heat sources and the working fluids, is analyzed with the capacity of heat exchanger as a design parameter. When the temperatures of heat sources and the input work are fixed as constants, the optimal design condition is obtained as an optimum ratio of capacities of heat exchangers, which is exactly unity when the exergy output and effectiveness are maximum. In addition, the optimum ratio is slightly increased from unity as the irreversibility of the cycle increases.