• Journal of Korean Institute of Industrial Engineers
• /
• v.29 no.4
• /
• pp.304-311
• /
• 2003

This paper considers a new variation of scheduling problems where jobs are dispatched in batches. The variation is the case where the batch sequence is fixed. The objective is to minimize the sum of the completion times of the batches. This simple environment has a variety of real world applications such as part kitting and customer order scheduling. We show that this problem is binary NP-complete when there exist two machines. For the same problem, we develop an optimal dynamic programming (DP) algorithm which runs in pseudo-polynomial time. We finally prove the optimality of the DP algorithm.

• Journal of Korea Water Resources Association
• /
• v.43 no.1
• /
• pp.67-79
• /
• 2010

Korea water resources corporation (K-Water) has developed the real-time water resources management system for the Nakdong and the Geum River basin to efficiently operate multi-purpose dams in the basins. This study has extended to the Han River basin for providing an effective ending target storage of a month to the real-time water resources management system using Sampling Stochastic Dynamic Programming (SSDP), consequently increasing the efficiency of the reservoir system. The optimization model were developed for three reservoirs, named Soyang, Chungju, and Hwacheon, with high priority in terms of the amounts of effective capacity and water supply for the basin. The number of storage state variable for each dam to set an optimization problem has been assigned from the results of sensitivity analysis. Compared with the K-water operating policy with the target water supply elevations, the optimization model suggested in this study showed that the shortfalls are decreased by 37.22 MCM/year for the required water demands in the basin, even increasing 171 GWh in hydro electronic power generation. In addition, the result of a reservoir operating system during the drawdown period applied to real situation demonstrates that additional releases for water quality or hydro electronic power generation would be possible during the drawdown period between 2007 and 2008. On the basis of these simulation results, the applicability of the SSDP model and the reservoir operating system is proved. Therefore, the more efficient reservoir operation can be achieved if the reservoir operating system is extended further to other Korean basins.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.570-574
• /
• 1990

Detection of left ventricular boundary for the functional analysis of LV(left ventricle)is obtained using automatic boundary detection algorithm based on dynamic programming method. This scheme reduces the edge searching time and ensures connective edge detection, since it does not require general edge operator, edge thresholding and linking process of other edge. detection methods. The left ventricular diastolic volume and systolic volume and systolic volume were computed after this automatic boundary detection, and these Volume data wm applied to analyze LV ejection fraction.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2007.11a
• /
• pp.508-509
• /
• 2007

For the safty driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates.

• Nuclear Engineering and Technology
• /
• v.55 no.8
• /
• pp.2952-2965
• /
• 2023

Advanced reactors, such as Small Modular Reactors or existing Nuclear Power Plants, often use Field Programmable Gate Array (FPGA) based controllers in new Instrumentation and Control (I&C) system architectures or as an alternative to existing analog-based I&C systems. Compared to CPU-based Programmable Logic Controllers (PLCs), FPGAs offer better overall performance. However, programming functions on FPGAs can be challenging due to the requirement for a hardware description language that does not explicitly support the operation of real numbers. This study aims to implement the Reactor Trip (RT) functions of the existing analog-based Reactor Protection System (RPS) using FPGAs. The RT equations for Overtemperature delta Temperature and Overpower delta Temperature involve dynamic compensators expressed with the Laplace transform variable, 's', which is not directly supported by FPGAs. To address this issue, the trip equations with the Laplace variable in the continuous-time domain are transformed to the discrete-time domain using the Z-transform. Additionally, a new operation based on a relative value for the equation range is introduced for the handling of real numbers in the RT functions. The proposed approach can be utilized for upgrading the existing analog-based RPS as well as digitalizing control systems in advanced reactor systems.

• The Journal of the Korea Contents Association
• /
• v.7 no.6
• /
• pp.44-51
• /
• 2007

For real-time recognizing of model objects in remote position a new Neural Networks algorithm is proposed. The proposed neural networks technique is the real time computation methods through the inter-node diffusion. In the networks, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of objects, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.620-626
• /
• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.805-812
• /
• 1989

An efficient diagnostic approach for real-time operation aiding expert system in chemical process plants is discussed. The approach is based on the hybrid of the simplified symptom tree(SST) and the fault consequence digraph(FCD), representation of propagation patterns of fault states. The SST generates fault hypothesis efficiently and the FCD resolve the real fault accurately. Frame based knowledge representation and object-oriented programming make diagnostic system general and efficient. Truth maintenance system enables robust pattern matching and provides enhanced explain facilities. A prototype expert system for supports operation of naphtha furnaces process, called OASYS, has been built and tested to demonstrate this methodology. Utilization of diversified process symbolic data, produced using dynamic normal standards, overcomes the problem of qualitative Boolean reasoning and enhance the applicability.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.8 no.1
• /
• pp.49-60
• /
• 1988

The use of an Incremental Dynamic Programming (IDP) for real-time flood control operation is investigated. The optimization model has been applied for the Namgang and Andong hypothetical flood control system in the Nakdong river basin. The objective of the operation is defined to minimize the maximum flow at the confluence of downstreams from the two reservoirs. The results are compared to the direct summation of the flood routing results from individual flood control simulation run. It shows that peak flow at the confluence is reduced markedly by reducing peak outflows from individual reservoirs and by balancing the time of the peak release between the two reservoirs.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.21 no.1
• /
• pp.62-72
• /
• 1997

High flexibility and productivity using industrial robots are being achieved in manufacturing lines with off-line robot programmings. A good off-line programming system should have functions of robot modelling, trajectory planning, graphical teach-in, kinematic and dynamic simulations. Simulated results, however, can hardly be applied to on-line tasks until any calibration procedure is accompained. This paper proposes a visual calibration scheme in order to provide a calibration tool for our own off-line programming system of SCARA robots. The suggested scheme is based on the position-based visual servoings, and the perspective projection. The scheme requires only one camera as it uses saved kinematic data for three-dimensional visual calibration. Predicted images are generated and then compared with camera images for updating positions and orientations of objects. The scheme is simple and effective enough to be used in real time robot programming.