• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.275-278
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• 2001

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Journal of Applied Reliability
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• v.9 no.2
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• pp.121-130
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• 2009

The power law process model the Rate of occurrence of failures(ROCOF) with monotonic trend during the operating time. However, the power law process is inappropriate when a non-monotonic trend in the failure data is observed. In this paper we deals with the reliability modeling of the failure process of large and complex repairable system whose rate of occurrence of failures shows the non-monotonic trend. We suggest a sectional model and a change-point test based on the Schwarz information criterion(SIC) to describe the non-monotonic trend. Maximum likelihood is also suggested to estimate parameters of sectional model. The suggested methods are applied to field data from an repairable system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1315-1329
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• 2018

Ciphertext-policy attribute-based encryption (CP-ABE) associates ciphertext with access policies. Only when the user's attributes satisfy the ciphertext's policy, they can be capable to decrypt the ciphertext. Expressivity and security are the two directions for the research of CP-ABE. Most of the existing schemes only consider monotonic access structures are selectively secure, resulting in lower expressivity and lower security. Therefore, fully secure CP-ABE schemes with non-monotonic access structure are desired. In the existing fully secure non-monotonic access structure CP-ABE schemes, the attributes that are set is bounded and a one-use constraint is required by these projects on attributes, and efficiency will be lost. In this paper, to overcome the flaw referred to above, we propose a new fully secure non-monotonic access structure CP-ABE. Our proposition enforces no constraints on the scale of the attributes that are set and permits attributes' unrestricted utilization. Furthermore, the scheme's public parameters are composed of a constant number of group elements. We further compare the performance of our scheme with former non-monotonic access structure ABE schemes. It is shown that our scheme has relatively lower computation cost and stronger security.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1553-1556
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• 2003

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.05a
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• pp.52-56
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• 2003

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.505-509
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• 2001

In this paper, we evaluate the learning ability of non-monotonic DMM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Journal of applied mathematics & informatics
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• v.29 no.1_2
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• pp.75-85
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• 2011

In this paper, using the energy estimates and the bootstrap arguments, the global existence of classical solutions for a prey-predator model with non-monotonic functional response and cross-diffusion where the prey and predator both have linear density restriction is proved when the space dimension n < 10.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.314-321
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• 1995

This paper discuss some properties of non-monotonic fuzzy measures of Ф -bounded variation. We show that there is an example of Ф such that $\beta$V(x, F) is a proper subspace of Ф$\beta$V(x, F) And also, we prove that Ф$\beta$V(x, F) is a real Banach space. Furthermore, we investigate some properties of non-monotonic fuzzy Ф -measures.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.321-331
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• 2024

We consider the numerical treatment of blow-up problems having non-monotonic singular solutions that tend to infinity at some point in the domain. The use of standard numerical methods for solving problems with blow-up solutions can lead to significant errors. The reason being that solutions of such problems have singularities whose positions are unknown in advance. To be able to integrate such non-monotonic blow-up problems, we describe and use a method of non-local transformations. To show the efficiency of the method, we present a comparison of exact and numerical solutions in addition to some comparison with the work of other authors.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.120-127
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• 2011

Checkpointing is one of common methods of realizing fault-tolerance for real-time systems. This paper presents a scheme to determine checkpoint intervals using probabilistic optimization. The considered real-time systems comprises multiple tasks in which transient faults can happen with a Poisson distribution. Also, multi-tasks are scheduled by the non-preemptive Rate Monotonic (RM) algorithm. In this paper, we present an optimization problem where the probability of task completion is described by checkpoint numbers. The solution to this problem is the optimal set of checkpoint numbers and intervals that maximize the probability. The probability computation includes schedulability test for the non-preemptive RM algorithm with respect to given numbers of checkpoint re-execution. A case study is given to show the applicability of the proposed scheme.