• ETRI Journal
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• v.21 no.3
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• pp.6-15
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• 1999

Many studies in the software reliability have attempted to develop a model for predicting the faults of a software module because the application of good prediction models provides the optimal resource allocation during the development period. In this paper, we consider the change request data collected from the field test of the software module that incorporate a functional relation between the faults and some software metrics. To this end, we discuss the general aspect if regression method, the problem of multicollinearity and the measures of model evaluation. We consider four possible regression models including two stepwise regression models and two nonlinear models. Four developed models are evaluated with respect to the predictive quality.

• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.66-74
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• 2008

Criticality prediction models to identify most fault-prone modules in the system early in the software development process help in allocation of resources and foster software quality improvement. Many models for identifying fault-prone modules using design complexity metrics have been suggested, but most of them are training models that need training data set. Most organizations cannot use these models because very few organizations have their own training data. This paper builds a prediction model based on a well-known supervised learning model, error backpropagation neural net, using design metrics quantifying SDL system specifications. To solve the problem of other models, this model is trained by generated virtual training data set. Some simulation studies have been performed to investigate feasibility of this model, and the results show that suggested model can be an alternative for the organizations without real training data to predict their software qualities.

• The Korean Society of Law and Medicine
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• v.11 no.1
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• pp.59-84
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• 2010

Medical practice is characterized by various physiological response and uncapacity of prediction, therefore when medical accident occur it's hard to prove medical professionals' mistake. Though medical accident by medical professionals' mistake will be compensated anyhow, about irresistible medical accidents, no one should be not bound to compensate, victims get into very difficult situation. So, the nation don't negligent irresistible medical accidents but compensate anyway. As in the past, to the legal principle's constitution of irresistible medical accidents, theory of liability without fault was adapted, and it was said this theory was illogical in theory of liability with fault. But the subject of compensation to irresistible medical accidents is nation, nation don't participate in medical treatment therefore there is no room to occur mistake. And it is not reasonable to regard medical agency as a truster of public service, to cast to it responsibility of medical accidents. The problem of compensation to irresistible medical accidents is understood under the theory of social compensation. Social compensation is consisted of compensation to sacrifice and contribution to nation and society and compensation to sacrifice revealed under danger, the compensation to irresistible medical accidents belongs to the latter. This is near to concept of relief, is applied to national compensation system supplementarily, and compensation have no option but to compensate minimum. And there are not relation between national compensation system of irresistible medical accidents and proof liability transposition and theory of liability with out fault, merely in side of sharing responsibility burden between medical treater and victim, it is reasonable to discuss transportation of proof liability and compulsive liability insurance together.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.689-700
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• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.115-120
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• 1997

System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.364-366
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• 2018

본 논문에서는 Digital Twin 기반 건설기계 지능화를 위한 실시간 건설기계 데이터 처리 및 이상 유무 예측 시스템을 제안한다. 이 시스템은 빅 데이터 분산처리 기반으로 실시간 스트리밍 처리가 가능하며, CEP(Complex Event Processing)의 Sliding Window Operator를 활용한 Rule 적용을 통해 건설기계 데이터 처리 및 분석한다. 분석된 결과로 건설기계의 실시간 이상 유무를 판단할 수 있으며, 결과를 기반으로 Deep Learning 기술을 적용하고 학습된 모델을 통해 건설기계의 이상 유무를 예측하여 원활한 부품관리를 할 수 있다.

• Electrical & Electronic Materials
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• v.11 no.11
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.199-207
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• 2010

Recently, large amount of information in IDS(Intrusion Detection System) can be un manageable and also be mixed with false prediction error. In this paper, we propose a data mining methodology for IDS, which contains uncertainty based on training process and post-processing analysis additionally. Our system is trained to classify the existing attack for misuse detection, to detect the new attack pattern for anomaly detection, and to define border patter between attack and normal pattern. In experimental results show that our approach improve the performance against existing attacks and new attacks, from 0.62 to 0.84 about 35%.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.301-310
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• 2022

Accurate prediction of mixed ground conditions ahead of a tunnel face is of vital importance for safe excavation using tunnel boring machines (TBMs). Previous studies have primarily focused on electrical resistivity surveys from the ground surface for geotechnical investigation. In this study, an FE (finite element) numerical model was developed to simulate electrical resistivity surveys for the prediction of risky mixed ground conditions in front of a tunnel face. The proposed FE model is validated by comparing with the apparent electrical resistivity values obtained from the analytical solution corresponding to a vertical fault on the ground surface (i.e., a simplified model). A series of parametric studies was performed with the FE model to analyze the effect of geological and sensor geometric conditions on the electrical resistivity survey. The parametric study revealed that the interface slope between two different ground formations affects the electrical resistivity measurements during TBM excavation. In addition, a large difference in electrical resistivity between two different ground formations represented the dramatic effect of the mixed ground conditions on the electrical resistivity values. The parametric studies of the electrode array showed that the proper selection of the electrode spacing and the location of the electrode array on the tunnel face of TBM is very important. Thus, it is concluded that the developed FE numerical model can successfully predict the presence of a mixed ground zone, which enables optimal management of potential risks.

• Economic and Environmental Geology
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• v.51 no.4
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• pp.393-400
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• 2018

Despite some skeptical views on the possibility of earthquake prediction, observation and evaluation of precursory changes have been continued throughout the world. In Korea, the public concern on the earthquake prediction has been increased after 2016 $M_L5.8$ and 2017 $M_L5.4$ earthquakes occurred in Gyeongju and Pohang, the southeastern part in Korea, respectively. In this study, the abnormal increase of groundwater level was observed before the 2016 $M_L5.8$ Gyeongju earthquake in a borehole located in 52 km away from the epicenter. The well was installed in the Yangsan fault zone, and equipped for the earthquake surveillance. The abnormal change in the well would seem to be a precursor, considering the hydrogeological condition and the observations from previous studies. It is necessary to set up a specialized council to support and evaluate the earthquake prediction and related researches for the preparation of future earthquake hazards.