• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• Korean Journal of Agricultural Science
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• v.7 no.2
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• pp.131-144
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• 1980

The Linear Regression Model to extend the monthly runoff data in the short-recorded river was proposed by the author in 1979. Here in this study generalization precedure is made to apply that model to any given river basin and to any given station. Lengthier monthly runoff data generated by this generalized model would be useful for water resources assessment and waterworks planning. The results are as follows. 1. This Linear Regression Model which is a transformed water-balance equation attempts to represent the physical properties of the parameters and the time and space varient system in catchment response lumpedly, qualitatively and deductively through the regression coefficients as component grey box, whereas deterministic model deals the foregoings distributedly, quantitatively and inductively through all the integrated processes in the catchment response. This Linear Regression Model would be termed "Statistically deterministic model". 2. Linear regression equations are obtained at four hydrostation in Geum-river basin. Significance test of equations is carried out according to the statistical criterion and shows "Highly" It is recognized th at the regression coefficients of each parameter vary regularly with catchment area increase. Those are: The larger the catchment area, the bigger the loss of precipitation due to interception and detention storage in crease. The larger the catchment area, the bigger the release of baseflow due to catchment slope decrease and storage capacity increase. The larger the catchment area, the bigger the loss of evapotranspiration due to more naked coverage and soil properties. These facts coincide well with hydrological commonsenses. 3. Generalized diagram of regression coefficients is made to follow those commonsenses. By this diagram, Linear Regression Model would be set up for a given river basin and for a given station (Fig.10).

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.830-839
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• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.483-503
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• 1997

One important question that arises when dealing with temporal databases in context of object-oriented systems is the method that associates time with attributes relationship semantics. Results of previous work about attribute versioning, particularity extending flat(First Normal Form: FNF) or nested(Non-First Normal Form: NFNF) relational models. are not applicable to temporal object-oriented databases. This is because object-oriented models provide more powerful constructs than traditional models for structuring complex objects. Therefore, this paper presents an formal approach for incorporating temporal extension to object-oriented databases. Our goal in this paper is to study temporal object-oriented database representation according to generalization, aggregation and association among objects. We define tile concepts of attribute versioning in temporal object-oriented model, and we concentrate on the representation of temporal relationship among objects. Another contribution of this paper is to specify time constraints on relationship semantics and analyze our model based on representation criteria. By means of formalizing tile temporal object oriented model, this paper can not only provide tile robust operating functions that design algebraic operators, but also entrance the reuse of modules.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.163-167
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• 1999

The relational data model can't be applied to CAD/CAM, CE, and CIM since it can't support the semantic model concept such as complex object, random data definition, manipulation, version control, generalization, aggregation and so on. In this paper, the IPCS(Integrated Production Control System) for hand-rail of ship is parted into several module according to process function. further more, the object oriented data model is designed with UML(Unified Modeling Language). UML is the object oriented design methodology of industrial standard that combines the Booch's methodology, the Rumbaugh's On(Object Modeling Technique), and the Jacobson's OOSE(Object-Oriented Software Engineering) methodology. The efficient management is expected with object-oriented data model construction, since this developed system can achieve efficient process control, system maintenance, repair and extension.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Information Systems Review
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• v.8 no.3
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• pp.135-152
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• 2006

This study has been performed to analyze and identify some factors which has impact on the level of consumers' acceptance for the mobile ads. For better results and more reasonable generalization of the results, this study targets consumers in two such countries as Korea and U.S.A. This research investigates the determinants of consumer's acceptance for the mobile Ads. Based on TAM(Technology Acceptance Model) and Flow theory, our research adopts some factors for a theoretical model such as entertainment, information, irritation, credibility, flow experience, attitudes and acceptance intention. For examining an implied relationships by this research model, a survey was conducted by collecting research data from students and consumers of mobile ads in Korea, and U.S.A. This data was analyzed based on using AMOS, the structural equation modeling and a second-generation multi variant technique, and has gained distinct advantages over other technique.

• The Journal of Korean Association of Computer Education
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• v.15 no.2
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• pp.37-45
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• 2012

The case of South Korea's Internet newspapers, harmful advertising is illegal but rampant. The children and youth are not protected, so effective measures are urgently required. Therefore, to achieve self-regulation, a discrimination system model using collective intelligence and collective emotions is proposed. This study is to suggest a Discrimination System Model of harmful contents using collective intelligence and collective emotions as the actual program of self-regulation. The Discrimination System model forms the level of harmful contents by using contents, form, text, size as well as the implied and reminiscent story of image as discriminant factors of a group testing. The formed level is established for harmful contents discriminant criteria after going through the process of generalization again. It can be not clear and ambiguous for internet newspaper banner ads to be measure the level of harmfulness. This Discrimination System will have the strengths of resolving this problem.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.76-82
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• 2010

In this paper, we develop Linguistic Model (LM) based on information granules as a systematic approach to generating fuzzy if-then rules from a given input-output data. The LM introduced by Pedrycz is performed by fuzzy information granulation obtained from Context-based Fuzzy Clustering(CFC). This clustering estimates clusters by preserving the homogeneity of the clustered patterns associated with the input and output data. Although the effectiveness of LM has been demonstrated in the previous works, it needs to improve in the sense of performance. Therefore, we focus on the automatic generation of linguistic contexts, addition of bias term, and the transformed form of consequent parameter to improve both approximation and generalization capability of the conventional LM. The experimental results revealed that the improved LM yielded a better performance in comparison with LM and the conventional works for automobile MPG(miles per gallon) predication and Boston housing data.