• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.1
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• pp.10-21
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• 2008

User travel behavior is based on the existence of complete traffic information in deterministic user optimal principle by Wardrop(1952). According to deterministic user optimal principle, users choose the optimal route from origin to destination and they change their routes arbitrarily in order to minimize travel cost. In this principle, users only consider travel time as a factor to take their routes. However, user behavior is not determined by only travel time in actuality. Namely, the models that reflect only travel time as a route choice factor could give irrational travel behavior results. Therefore, the model is necessary that considers various factors including travel time, transportation networks structure and traffic information. In this research, more realistic deterministic optimal traffic assignment model is proposed in the way of route recognizance behavior. This model assumes that when users decide their routes, they consider many factors such as travel time, road condition and traffic information. In addition, route recognizance attributes is reflected in this suggested model by forward searching method and backward searching method with numerical formulas and algorithms.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.83-92
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• 2023

When assessing the risk of radioactive wastes transportation on land, computer codes such as RADTRAN and RISKIND are used as deterministic methods. Transportation risk assessment using the deterministic method requires a relatively short assessment time. On the other hand, transportation risk assessment using the probabilistic method requires a relatively long assessment time, but produces more reliable results. Therefore, a study is needed to evaluate the exposure dose using a deterministic method that can be evaluated relatively quickly, and to compare and analyze the exposure dose result using a probabilistic method. The purpose of this study is to evaluate the exposure dose during transportation of radioactive wastes using deterministic and probabilistic methods, and to compare and analyze them. For this purpose, the main exposure factors were selected and various exposure situations were set. The distance between the radioactive waste and the receptor, the size of the package, and the speed of vehicle were selected as the main exposure factors. The exposure situation was largely divided into when the radioactive wastes were stationary and when they were passing. And the dose (rate) model of the deterministic overland transportation risk assessment computer code was analyzed. Finally, the deterministic method of the RADTRAN computer code and the RISKIND computer code and the probabilistic method of the MCNP 6 computer code were used to evaluate the exposure dose in various exposure situations during transportation of radioactive wastes. Then we compared and analyzed them. As a result of the evaluation, the tendency of the exposure dose (rate) was similar when the radioactive wastes were stationary and passing. For the same situation, the evaluation results of the RADTRAN computer code were generally more conservative than the results of the RISKIND computer code and the MCNP 6 computer code. The evaluation results of the RISKIND computer code and the MCNP 6 computer code were relatively similar. The results of this study are expected to be used as basic data for establishing the radioactive wastes transportation risk assessment system in Korea in the future.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.49-60
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• 2023

The amount of data generated by electronic systems through e-commerce, social networks, and data computation has risen. However, the security of data has always been a challenge. The problem is not with the quantity of data but how to secure the data by ensuring its confidentiality and privacy. Though there are several research on cloud data security, this study proposes a security scheme with the lowest execution time. The approach employs a non-linear time complexity to achieve data confidentiality and privacy. A symmetric algorithm dubbed the Non-Deterministic Cryptographic Scheme (NCS) is proposed to address the increased execution time of existing cryptographic schemes. NCS has linear time complexity with a low and unpredicted trend of execution times. It achieves confidentiality and privacy of data on the cloud by converting the plaintext into Ciphertext with a small number of iterations thereby decreasing the execution time but with high security. The algorithm is based on Good Prime Numbers, Linear Congruential Generator (LGC), Sliding Window Algorithm (SWA), and XOR gate. For the implementation in C, thirty different execution times were performed and their average was taken. A comparative analysis of the NCS was performed against AES, DES, and RSA algorithms based on key sizes of 128kb, 256kb, and 512kb using the dataset from Kaggle. The results showed the proposed NCS execution times were lower in comparison to AES, which had better execution time than DES with RSA having the longest. Contrary, to existing knowledge that execution time is relative to data size, the results obtained from the experiment indicated otherwise for the proposed NCS algorithm. With data sizes of 128kb, 256kb, and 512kb, the execution times in milliseconds were 38, 711, and 378 respectively. This validates the NCS as a Non-Deterministic Cryptographic Algorithm. The study findings hence are in support of the argument that data size does not determine the execution.

• Journal of the Korean Operations Research and Management Science Society
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• v.4 no.1
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• pp.69-77
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• 1979

A single machine stochastic scheduling problem is considered. Associated with each job is its random processing time and random deferral cost. The criterion is to order the jobs so as to minimize the sum of the deferral costs. The expected sum of the deferral costs is theroretically derived under the stochastic situation for each of several scheduling decision rules which are well known for the deterministic environment. It is also shown that certain stochastic problems can be reduced to equivalent deterministic problems. Two examples are illustrated to show the expected total deferral costs.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.73-82
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• 2007

In recent years, embedded systems have been expanding their application domains from traditional applications (such as defense, robots, and artificial satellites) to portable devices which execute more complicated applications such as cellular phones, digital camcoders, PMPs, and MP3 players. So as to manage restricted hardware resources efficiently and to guarantee both temporal and logical correctness, every embedded system use a real-time operating system (RTOS). Only when the RTOS makes kernel services deterministic in time by specifying how long each service call will take to execute, application programers can write predictable applications. Moreover, so as for an RTOS to be deterministic, its scheduling and context switch overhead should also be predictable. In this paper, we present the complete generalized algorithm to determine the highest priority in the ready list with 22r levels of priorities in a constant time without additional memory overhead.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1758-1764
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• 2012

A large number of wire rope has been used in various inderstiries as Cranes and Elevators from expanding the scale of the industrial market. But now, the management of wire rope is used as manually operated by rope replacement from over time or after the accident.It is caused to major accidents as well as economic losses and personal injury. Therefore its time to need periodic fault diagnosis of wire rope or supply of real-time monitoring system. Currently, there are several methods has been reported for fault diagnosis method of the wire rope, to find out the feature point from extracting method is becoming more common compared to time wave and model-based system. This method has implemented a deterministic modeling like the observer and neural network through considering the state of the system as a deterministic signal. However, the out-put of real system has probability characteristics, and if it is used as a current method on this system, the performance will be decreased at the real time. And if the random noise is occurred from unstable measure/experiment environment in wire rope system, diagnostic criterion becomes unclear and accuracy of diagnosis becomes blurred. Thus, more sophisticated techniques are required rather than deterministic fault diagnosis algorithm. In this paper, we developed the fault diagnosis of the wire rope using probability density estimation techniques algorithm. At first, The steady-state wire rope fault signal detection is defined as the probability model through probability distribution estimate. Wire rope defects signal is detected by a hall sensor in real-time, it is estimated by proposed probability estimation algorithm. we judge whether wire rope has defection or not using the error value from comparing two probability distribution.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.3
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• pp.265-274
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• 2010

In many tunnels, falling or sliding of rock blocks often occur, which cannot be predicted because of the complexity of rock discontinuities and it has brought an exponential increase in costs and time to manage. It is difficult to estimate the properties of rock masses before the tunnel excavation. The observational design and construction method in tunnels has been becoming important recently. In this study, a new hybrid stochastic-deterministic rock block analysis method for the prediction of the unstable rock blocks before the tunnel excavation is proposed, and then applied to the tunnel construction based on actual rock discontinuity information observed in the field. The comparisons and investigations with the analytical results in the tunnel construction have confirmed the validity and applicability of this new hybrid stochastic-deterministic rock block analysis method in tunnels.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.2
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• pp.288-293
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• 2017

Transient simulation capability with an implicit time derivation method is a missing feature in deterministic Boltzmann equation solvers. The H-transformation, which is critical for the stable simulation of nanoscale devices, introduces difficulties for the transient simulation. In this work, the transient simulation of graphene sheets is reported. It is shown that simulation of homogeneous systems can be done without abandoning the H-transformation, as much as a specially designed discretization method is employed. The AC mobility and step response of the graphene sheet on the $SiO_2$ substrate are simulated.

• Korean Management Science Review
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• v.17 no.2
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• pp.175-187
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• 2000

The stochastic vehicle routing problem (VRP) is a problem of growing importance since it includes a reality that the deterministic VRP does not have. The stochastic VRP arises whenever some elements of the problem are random. Common examples are stochastic service quantities and stochastic travel times. The solution methodologies for the stochastic VRP are very intricate and regarded as computationally intractable. Even heuristics are hard to develope and implement. On possible way of solving it is to apply a solution for the deterministic VRP. This paper presents a performance evaluation of four simple heuristic for the deterministic VRP is a stochastic environment. The heuristics are modified to consider the time window constraints. The computational results show that some of them perform very well in different cases of the stochastic VRP.