• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1479-1501
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• 2020

Dependable functioning of dynamic networks is essential for delivering ubiquitous services. Faults are the root causes of network outages. The comparison diagnosis model, which automates fault's identification, is one of the leading approaches to attain network dependability. Most of the existing research has focused on stationary networks. Nonetheless, the time-free comparison model imposes no time constraints on the system under considerations, and it suits most of the diagnosis requirements of dynamic networks. This paper presents a novel protocol that diagnoses faulty nodes in diagnosable dynamic networks. The proposed protocol comprises two stages, a testing stage, which uses the time-free comparison model to diagnose faulty neighbour nodes, and a disseminating stage, which leverages a Random Linear Network Coding (RLNC) technique to disseminate the partial view of nodes. We analysed and evaluated the performance of the proposed protocol under various scenarios, considering two metrics: communication overhead and diagnosis time. The simulation results revealed that the proposed protocol diagnoses different types of faults in dynamic networks. Compared with most related protocols, our proposed protocol has very low communication overhead and diagnosis time. These results demonstrated that the proposed protocol is energy-efficient, scalable, and robust.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.6
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• pp.117-125
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• 2012

This paper describes a model-based software development methodology for AUTOSAR-ready light software architecture(AUTOSAR-Lite). The proposed methodology briefly represents an application software modeling technique using Matlab/Simulink. Using the proposed technique, application software architecture elements (e.g. software components, runnables, and interfaces) and functional behaviors can be designed in a single modeling environment. From the designed model, the codes of application software is automatically generated using Real-Time Workshop Embedded Coder. The generated application software is easily integrated with hand-coded basic software using the proposed method. In order to evaluate the proposed methodology, a diesel engine management system for a passenger car was employed as a case study. Based on the methodology, 8 atomic software components and 52 runnables are successfully developed, and they are evaluated by engine experiments. From this case study, AUTOSAR compatible model-based application software was successfully developed, and the effectiveness of the proposed methodology was evaluated.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1571-1578
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• 2013

With the emergence of high-integration array and large area panel process, the need to minimize the generation of particles in the field of semiconductor, LCD and OLED has grown. As an alternative to the conventional roller system, a contactless magnetic conveyor has been proposed to reduce the generation of particles. An EM-PM hybrid which is one of magnetic levitation types is already proposed for the conveyor system. One of problems pointed out with this approach is the vibration caused by the dynamic interaction between conveyor and rail. To reduce the vibration, the introduction of a secondary suspension system which aims to decouple the levitation electromagnet from the main body is proposed. The objective of this study is to develop a dynamic model for the magnetically levitated conveyor, and to investigate the effect of the introduced suspension system. An integrated model of levitation system and rail based on 3D multibody dynamic model is proposed. With the proposed model, the dynamic characteristics of maglev conveyor system are analyzed, and the effect of the secondary suspension and the stiffness and damping are investigated.

• Journal of Institute of Convergence Technology
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• v.6 no.2
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• pp.29-33
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• 2016

Recently, the authors of this paper proposed newly the correlation model to calculate the horizontal global solar radiation in Korea based the Zhang-Huang (ZH) model proposed in 2002 for China. Previous study was pronounced the correlation with a new term of the duration of sunshine proved as being closely related with the hourly solar radiation in Korea into ZH model. And then another modified correlation for the regions without measuring cloud cover was proposed and evaluated the accuracy and validity for those regions. So, this study was performed to propose modified correlation to calculate the horizontal global solar irradiance of non-measuring cloud-cover regions. Finally, this study proposed the new correlation that could well predict hourly and daily total solar radiation for all regions, various seasons, and various weather conditions including overcast and clear, with higher accuracy and lower error than other models proposed ever before in Korea for non-measuring cloud-cover regions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.407-414
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• 2007

A sensor fault detection scheme(SFDS) for a class of nonlinear systems that can be represented by Takagi-Sugeno fuzzy model is proposed. Basically, the SFDS may be considered as a multiple observer scheme(MOS) in which the bank of state observers and the detection & isolation logic are included. However, the proposed scheme has two great differences from the conventional MOSs. First, the proposed scheme includes fuzzy fault detection observers(FFDO) that are constructed based on the T-S fuzzy model that provides very good approximation to nonlinear dynamic systems. Secondly, unlike the conventional MOS, the FFDOS are driven not parallelly but sequentially according to the predetermined sequence to avoid the massive computational burden, which is known to be the biggest obstacle to the practical application of the multiple observer based FDI schemes. During the operating time, each FFDO generates the residuals carrying the information of a specified fault, and the corresponding fault detection logic unit performs the logical operations to detect and isolate the fault of interest. The proposed scheme is applied to an inverted pendulum control system for sensor fault detection/isolation. Simulation study shows the practical feasibility of the proposed scheme.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.3
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• pp.107-117
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• 2000

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.12
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• pp.705-712
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• 2003

In this paper, we propose new unified methodologies of reliability and its cost evaluation in power distribution systems. The unified method means that the proposed reliability approaches consider both conventional evaluation factor, i.e. sustained interruptions and additional ones, i.e. momentary interruptions and voltage sags. Because the three voltage quality phenomena generally originate from the outages on distribution systems, the basic and additional reliability indices are summarized considering the fault clearing mechanism. The proposed unified method is divided into the reliability evaluation for calculating the reliability indices and reliability cost evaluation for assessing the damage of customer. The analytic and probabilistic methodologies are presented for each unified reliability and its cost evaluation. The time sequential Monte Carlo technique is used for the probabilistic method. The proposed DVL(Demand Varying Load) model is added to the reliability cost evaluation substituting the average load model. The proposed methods are tested using the modified RBTS(Roy Billinton Test System) form and historical reliability data of KEPCO(Korea Electric Power Corporation) system. The daily load profile of the each customer type in domestic are gathered for the DVL model. Through the case studies, it is verified that the proposed methods can be effectively applied to the distribution systems for more detail reliability assessment than conventional approaches.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.10
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• pp.1469-1478
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• 2015

In this paper, The methodology of Type-2 fuzzy set-based Radial Basis Function Neural Network(T2RBFNN) is proposed for Sewage Treatment Process and the simulator is developed for application to the real-world sewage treatment plant by using the proposed model. The proposed model has robust characteristic than conventional RBFNN. architecture of network consist of three layers such as input layer, hidden layer and output layer of RBFNN, and Type-2 fuzzy set is applied to receptive field in contrast with conventional radial basis function. In addition, the connection weights of the proposed model are defined as linear polynomial function, and then are learned through Back-Propagation(BP). Type reduction is carried out by using Karnik and Mendel(KM) algorithm between hidden layer and output layer. Sewage treatment data obtained from real-world sewage treatment plant is employed to evaluate performance of the proposed model, and their results are analyzed as well as compared with those of conventional RBFNN.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• Structural Engineering and Mechanics
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• v.73 no.3
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• pp.287-302
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• 2020

This investigation deals with a size-dependent coupled thermoelasticity analysis based on Green-Naghdi (GN) theory in nano scale using a new modified nonlocal model of heat conduction, which is based on the GN theory and nonlocal Eringen theory of elasticity. In the analysis based on the proposed model, the nonlocality is taken into account in both heat conduction and elasticity. The governing equations including the equations of motion and the energy balance equation are derived using the proposed model in a nano beam resonator. An analytical solution is proposed for the problem using the Laplace transform technique and Talbot technique for inversion to time domain. It is assumed that the nano beam is subjected to sinusoidal thermal shock loading, which is applied on the one of beam ends. The transient behaviors of fields' quantities such as lateral deflection and temperature are studied in detail. Also, the effects of small scale parameter on the dynamic behaviors of lateral deflection and temperature are obtained and assessed for the problem. The proposed GN-based model, analytical solution and data are verified and also compared with reported data obtained from GN coupled thermoelasticity analysis without considering the nonlocality in heat conduction in a nano beam.