• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.426-428
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• 2012

In order to identify proteins that are present in biological samples, these samples are separated and analyzed under the sequential procedure as follows: protein purification and digestion, peptide fragmentation by tandem mass spectrometry (MS/MS) which breaks peptides into fragments, peptide identification, and protein identification. One of the widely used methods for protein identification is based on probabilistic approaches such as ProteinProphet and BaysPro. However, they do not consider the difference in peptide identification probabilities according to their length. Here, we propose a probabilistic graphical model-based approach to protein identification from MS/MS data considering peptide identification probabilities, number of sibling peptides, and peptide length. We compared our approach with ProteinProphet using a yeast MS/MS dataset. As a result, our model identified 27 more proteins than ProteinProphet at 1% of FDR (false discovery rate), confirming the importance of peptide length information in protein identification.

• Journal of Communications and Networks
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• v.16 no.5
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• pp.534-547
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• 2014

Large-scale failures resulting from natural disasters or intentional attacks are now causing serious concerns for communication network infrastructure, as the impact of large-scale network connection disruptions may cause significant costs for service providers and subscribers. In this paper, we propose a new framework for the analysis and prevention of network service disruptions in large-scale failure scenarios. We build dynamic deterministic and probabilistic models to capture the impact of regional failures as they evolve with time. A probabilistic failure model is proposed based on wave energy behaviour. Then, we develop a novel approach for preventive protection of the network in such probabilistic large-scale failure scenarios. We show that our method significantly improves uninterrupted delivery of data in the network and reduces service disruption times in large-scale regional failure scenarios.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.756-764
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• 2003

We investigate a probabilistic public key cryptosystem proposed by Paillier. It is based on the discrete logarithmic function and the messages are calculated from the modular product of two those functions, one of which has a fixed value depending on a given public key. The improvement is achieved by a good choice for the public key so that it is possible to get efficient schemes without losing the onewayness and semantic security. Also we suggest the method to get the public key for our schemes.

• Journal of Information Technology Applications and Management
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• v.16 no.3
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• pp.59-71
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• 2009

Hazard analysis identifies probability to hazard occurrence and its potential impact on business processes operated in organizations. This paper illustrates a quantitative approach of hazard analysis of information systems by measuring the degree of hazard to information systems using probabilistic risk analysis and activity based costing technique. Specifically the research model projects probability of occurrence by PRA and economic loss by ABC under each identified hazard. To verify the model, each computerized subsystem which is called a business process and hazards occurred on information systems are gathered through one private organization. The loss impact of a hazard occurrence is produced by multiplying probability by the economic loss.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.206-209
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• 2001

This paper illustrates a new method for reliability evaluation at load points in a composite power system. The algorithm includes uncertainties of generators and transmission lines as well as main transformers at substations. The CMELDC based on the new effective load model at HLII has been developed also. The CMELDC can be obtain from convolution integral processing of the outage capacity probabilistic distribution function of the fictitious generator and the original load duration curve given at the load point. The CMELDC based on the new model at HLII will extend the application areas of nodal probabilistic production cost simulation, outage cost assessment and reliability evaluation etc. at load points. The characteristics and effectiveness of this new model are illustrated by a case study of a small test system.

• Structural Engineering and Mechanics
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• v.31 no.2
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• pp.153-162
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• 2009

The reduction of the dynamic response of an offshore structure subjected to wind-generated random ocean waves is of extreme significance in the aspects of serviceability, fatigue life and safety of the structure. In this study, a new neuro-control scheme is applied to the vibration control of a fixed offshore platform under random wave loads to examine the applicability of the proposed method. It is called the Lattice Probabilistic Neural Network (LPNN), as it utilizes lattice pattern of state vectors as the training data of PNN. When control results of the LPNN are compared with those of the NN and PNN, LPNN showed better performance in effectively suppressing the structural responses in a shorter computational time.

• Phonetics and Speech Sciences
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• v.9 no.2
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• pp.85-94
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• 2017

Conventional speaker verification systems using time delay neural network, identity vector and probabilistic linear discriminant analysis (TDNN-Ivector-PLDA) are known to be very effective for verifying long-duration speech utterances. However, when test utterances are of short duration, duration mismatch between enrollment and test utterances significantly degrades the performance of TDNN-Ivector-PLDA systems. To compensate for the I-vector mismatch between long and short utterances, this paper proposes to use probabilistic linear discriminant analysis (PLDA) model adaptation with augmented data. A PLDA model is trained on vast amount of speech data, most of which have long duration. Then, the PLDA model is adapted with the I-vectors obtained from short-utterance data which are augmented by using vocal tract length perturbation (VTLP). In computer experiments using the NIST SRE 2008 database, the proposed method is shown to achieve significantly better performance than the conventional TDNN-Ivector-PLDA systems when there exists duration mismatch between enrollment and test utterances.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1494-1503
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• 2018

This paper proposes a novel probabilistic load flow model for power systems integrated with large-scale wind power, which considers the multi-time scale dispatching features. The ramp limitations of the units and the steady-state security constraints of the network have been comprehensively considered for the entire duration of the study period; thus, the coupling of the system operation states at different time sections has been taken into account. For each time section, the automatic generation control (AGC) strategy is considered, and all variations associated with the wind power and loads are compensated by all AGC units. Cumulants and the Gram-Charlier expansion are used to solve the proposed model. The effectiveness of the proposed method is validated using the modified IEEE RTS 24-bus system and the modified IEEE 118-bus system.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.134-136
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• 2003

The deregulated electricity market is operated with respect to theory of economical efficiency, and therefore, the system operator requires data with fast contingency ranking for security of the bulk power system. This paper compares the weather dependant probabilistic risk index(PRI) with the system performance index for power flow in the IEEE-RBTS. also, the system performance index for power flow presents the power system stability. The probabilistic risk index can be classified into normal weather and adverse weather. This paper proposes calculation method using the probabilistic risk index in determining contingency ranking requiring for security under the deregulated electricity market.

• Earthquakes and Structures
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• v.22 no.2
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• pp.147-155
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• 2022

Regarding the importance of seismic pounding, the available standards and guidelines specify minimum separation distance between adjacent buildings. However, the rules in this field are generally based on some simple assumptions, and the level of confidence is uncertain. This is attributed to the fact that the relative response of adjacent structures is strongly dependent on the frequency content of the applied records and the Eigen frequencies of the adjacent structures as well. Therefore, this research aims at investigating the separation distance of the buildings through a probabilistic-based algorithm. In order to empower the algorithm, the record-to-record uncertainties, are considered by probabilistic approaches; besides, a wide extent of material nonlinear behaviors can be introduced into the structural model by the implementation of the hysteresis Bouc-Wen model. The algorithm is then simplified by the application of the linearization concept and using the response acceleration spectrum. By implementing the proposed algorithm, the separation distance in a specific probability level can be evaluated without the essential need of performing time-consuming dynamic analyses. Accuracy of the proposed method is evaluated using nonlinear dynamic analyses of adjacent structures.