• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.219-224
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• 2017

In facing the lack of a deterministic algorithm for economic load dispatch optimization problem, only non-deterministic heuristic algorithms have been suggested. Worse still, there is a near deficiency of research devoted to real-time load dispatch optimization algorithm. In this paper, therefore, I devise a shut-off and swap algorithm to solve real-time load dispatch optimization problem. With this algorithm in place, generators with maximum cost-per-unit generation power are to be shut off. The proposed shut-off criteria use only quadratic function in power generation cost function without valve effect nonlinear absolute function. When applied to the most prevalent economic load dispatch benchmark data, the proposed algorithm is proven to largely reduce the power cost of known algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.128-132
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• 2011

Least mean square adaptive algorithms have played important role in radar, sonar, speech processing, and mobile communication. In mobile communication area, the convergence rate of a LMS algorithm is quite important. However, LMS algorithms have slow and non-uniform convergence rate problem For overcoming these shortcomings, various variable step LMS adaptive algorithms have been studied in recent years. Most of these recent LMS algorithms have used complex variable step methods to get a rapid convergence. But complex variable step methods need a high computational complexity. Therefore, the main merits such as the simplicity and the robustness in a LMS algorithm can be eroded. The proposed deterministic variable step LMS algorithm is based upon a simple deterministic function for the step update so that the simplicity of the proposed algorithm is obtained and the fast convergence is still maintainable.

• Korean Journal of Construction Engineering and Management
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• v.2 no.2 s.6
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• pp.90-97
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• 2001

The paper considers non-deterministic methods of analysing the risk exposure in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The Monte Carlo method is popular method for incorporating uncertainty relative to parameter values in risk assessment modelling. Non-deterministic methods, they are here presented as possibly effective foundation on which to risk management in cost estimating. The objectives of this research is to develop a computerized algorithms to forecast the probabilistic total construction cost and the elemental work cost at the planning stage.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.11 no.2
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• pp.84-95
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• 2007

Background and Purpose: We have studied the trends of EEG signals in the voluntary breathing condition by applying the fractal analysis. According to chaos theory, irregularity of EEG signals can result from low dimensional deterministic chaos. A principal parameter to quantify the degree of Chaotic nonlinear dynamics is correlation dimension. The aim of this study was to analyze correlation between the correlation dimension of EEG and HRV(heart rate variability). We have studied the trends of EEG signals in the voluntary breathing condition by applying the fractal analysis. Methods: EEG raw data were measured by moving windows during 15 minutes. Then, the correlation dimension(D2) was calculated by each 40-seconds-segment in 15 minutes data, totally 36 segments. 8 channels EEG study on the Fp, F, T, P was carried out in 30 subjects. Results and Conclusion: Correlation analysis of HRV was calculated with deterministic non-linear data and stochastic non-linear data. 1. Ch1(Fp1), Ch4(F3), Ch4(F4) is positive correlated with In LF. 2. Ch1(Fp1), Ch3(F3) is positive correlated with In TF.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.9
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• pp.424-433
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• 2001

The cyclic debugging approach often fails for message passing parallel programs because they non-deterministic characteristics due to message race conditions. This paper identifies the MPI events that affect non-deterministic executions, and then converts the concurrent execution to the sequential one that is controlled in order to make it equivalent to a reference execution by keeping their orders of events in two executions identical. This paper also presents an efficient algorithm for the causal distributed breakpoint which is initiated by any sequential breakpoint in one process, and restores each process to the earliest state that reflects all events that happened causally before the sequential breakpoint. So a cyclic debugging approach can be used in debugging MPI parallel programs as like as in debugging sequential programming environments.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.93-99
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• 2021

The sensor networks should be appropriately designed by applied network purpose, so that they can support proper application functions. Based on the design of suitable network model, the network lifetime can be maximized than using other general strategies which have not the consideration of specific network environments. In this paper, we propose a non-deterministic agent scheme to the mobile sink in distributed wireless sensor networks. The sensor network area can be divided into several sensor regions. Hence, to these such networks, the specified suitable scheme is requested by the applied network model to implement satisfactory network management. In this paper, we theoretically represent the proposed scheme, and provide the evaluation with the simulation results.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.455-462
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• 2001

In this paper, we propose a deterministic algorithm to solve the resource discovery problem, that is, some subset of machines to learn the existence of each other in a large distributed network. Harchol et al. proposed a randomized algorithm solving this problem within O($log^2\;n$) rounds with high probability, which requires O($nlog^2\;n$) connection communication complexity and O($n^2log^2\;n$) pointer communication complexity, where n is the number of machines in the network. His solution is based on randomization method and it is difficult to determine convergence time. We propose an efficient algorithm which improve performance and the non-deterministic characteristics. Our algorithm requires O(log n) rounds which shows O(mlog n) connection communication complexity and O($n^2log\;n$) pointer communication complexity, where m is the number of links in the network.

• Nuclear Engineering and Technology
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• v.52 no.3
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• pp.499-507
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• 2020

Research reactors for radioisotope production, fuel and material testing and research activities are designed, constructed and operated based on the society's needs. In this study, neutronic and thermal hydraulic design of a high neutron flux research reactor core for radioisotope production is presented. Main parameters including core excess reactivity, reactivity variations, power and flux distribution during the cycle, axial and radial power peaking factors (PPF), Pu₂₃₉ production and minimum DNBR are calculated by nuclear deterministic codes. Core calculations performed by deterministic codes are validated with Monte Carlo code. Comparison of the neutronic parameters obtained from deterministic and Monte Carlo codes indicates good agreement. Finally, subchannel analysis performed for the hot channel to evaluate the maximum fuel and clad temperatures. The results show that the average thermal neutron flux at the beginning of cycle (BOC) is 1.0811 × 10¹⁴ n/㎠-s and at the end of cycle (EOC) is 1.229 × 10¹⁴ n/㎠-s. Total Plutonium (Pu₂₃₉) production at the EOC evaluated to be 0.9487 Kg with 83.64% grade when LEU (UO₂ with 3.7% enrichment) used as fuel. This designed reactor which uses LEU fuel and has high neutron flux and low plutonium production could be used for peaceful nuclear activities based on nuclear non-proliferation treaty concepts.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Genomics & Informatics
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• v.1 no.1
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• pp.20-24
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• 2003

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non­zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.