• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1798-1804
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• 2017

This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm's performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1709-1718
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• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1729-1742
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• 2017

In designing System Protection Schemes (SPSs) in power systems, protecting transmission network against extreme undesired conditions becomes a significant challenge in mitigating the transmission line overloading. This paper presents an intelligent Special Protection and Control Scheme (SPCS) using of Differential Evolution with Adaptive Mutation (DEAM) approach to obtain the optimum generation rescheduling to solve the transmission line overloading problem in system contingency conditions. DEAM algorithm employs the attraction-repulsion idea that is applied in the electromagnetism-like algorithm to support the mutation process of the conventional Differential Evolution (DE) algorithm. Different N-1 contingency conditions under base and increase load demand are considered in this paper. Simulation results have been compared with those acquired from Genetic Algorithm (GA) application. Minimum severity index has been considered as the objective function. The final results show that the presented DEAM method offers better performance than GA in terms of faster convergence and less generation fuel cost. IEEE 30-bus test system has been used to prove the effectiveness and robustness of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1934-1944
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• 2017

As a key component of high-voltage power conversion system for electric vehicles (EVs), bidirectional DC/DC (Bi-DC/DC) is required to have high efficiency and light weight. Conventional design methods optimize the Bi-DC/DC at the maximum power dissipation point (MPDP). For EVs application, the work condition of the Bi-DC/DC is not strict as the MPDP, where the design method using MPDP may not be optimal during travel of EVs. This paper optimizes the Bi-DC/DC converter targeting efficiency and weight based on the driving cycle. By analyzing the two-phase interleaved Bi-DC/DC for hybrid energy storage systems (HESS) of EVs, its power dissipation is calculated, and an efficiency model is derived. On this basis, weight models of capacitor, inductor and heat sink are built, as well as a dynamic temperature model of heat sink. Based on these models, a method using New European Driving Cycle (NEDC) for optimal design of Bi-DC/DC which simultaneously considered efficiency and weight is proposed. The simulation result shows that compare with conventional optimization methods revealed that the optimization approach based on driving cycle allowed significant weight reduction while meeting the efficiency requirements.

• Korean Journal of Geomatics
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• v.3 no.2
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• pp.129-140
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• 2004

This paper addresses solutions th the challenges of carrier phase integer ambiguity resolution and cycle slip detection/identification, for maintaining high accuracy of an integrated GPS/Pseudolite/INS system. Such a hybrid positioning and navigation system is an augmentation of standard GPS/INS systems in localized areas. To achieve the goal of high accuracy, the carrier phase measurements with correctly estimated integer ambiguities must be utilized to update the system integration filter's states. The contribution presents an effective approach to increase the reliability and speed of integer ambiguity resolution through using pseudolite and INS measurements, with special emphasis on reducing the ambiguity search space. In addition, an algorithm which can effectively detect and correct the cycle slips is described as well. The algorithm utilizes additional position information provided by the INS, and applies a statistical technique known as th cumulative-sun (CUSUM) test that is very sensitive to abrupt changes of mean values. Results of simulation studies and field tests indicate that the algorithms are performed pretty well, so that the accuracy and performance of the integrated system can be maintained, even if cycle slips exist in the raw GPS measurements.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.163-173
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• 1997

Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.

• Journal of Microbiology and Biotechnology
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• v.19 no.3
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• pp.229-237
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• 2009

Among 12 marine bacterial strains from the China coast that exhibited interesting bioactivity (positive for both antimicrobial and cytotoxic activities), only four strains, namely, NJ6-3-1, NJ6-3-2, NB-6, and YTHM-17, had a KS domain or A domain when screened for PKS and NRPS genes using a PCR. Interestingly, two of these strains belonging to Pseudoalteromonas and associated with the marine sponge Hymeniacidon perleve were positive for both PKS and NRPS, whereas the other two strains of Pseudoalteromonas did not have a PKS or NRPS gene. A molecular phylogeny analysis and DGGE analysis of the Pseudoalteromonas sp. indicated that they had a specific affinity with the host marine sponge Hymeniacidon perleve. Furthermore, an analysis of a partial sequence of Pseudoalteromonas sp. NJ6-3-2 isolated from the marine sponge Hymeniacidon perleve obtained from genomic walking using a computational approach indicated a relatively complete PKS module including auxiliary domains (DH, KR, and Cy).

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.601-613
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• 2017

An adjoint-based error estimation and mesh adaptation study is conducted for two-dimensional viscous flows on unstructured hybrid meshes. The error in an integral output functional of interest is estimated by a dot product of the residual vector and adjoint variable vector. Regions for the mesh to be adapted are selected based on the amount of local error at each nodal point. Triangular cells in the adaptive regions are refined by regular refinement, and quadrangular cells near viscous walls are bisected accordingly. The present procedure is applied to single-element airfoils such as the RAE2822 at a transonic regime and a diamond-shaped airfoil at a supersonic regime. Then the 30P30N multi-element airfoil at a low subsonic regime with a high incidence angle (${\alpha}=21deg.$) is analyzed. The same level of prediction accuracy for lift and drag is achieved with much less mesh points than the uniform mesh refinement approach. The detailed procedure of the adjoint-based mesh refinement for the multi-element airfoil case show that the basic flow features around the airfoil should be resolved so that the adjoint method can accurately estimate an output error.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.157-167
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• 2005

In this paper, we propose a new phantom protection method for multi-dimensional index structures. The proposed method uses a hybrid approach of predicate locking and granular locking mechanisms. The proposed mechanism is independent of the types of multi-dimensional index structures, i.e., it can be applied to all types of index structures such as tree-based, file-based and hash-based index structures. Also, it achieves low development cost and high concurrency with low lock overhead. It is shown through various experiments that the proposed method outperforms existing phantom protection methods for multi-dimensional index structures.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1388-1395
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• 2020

Daily monitoring of the heart rate can facilitate detection of heart-related diseases in their early stages. Therefore, providing an easy-to-use and noninvasive heart rate monitoring system has been a very popular research topic in the field of healthcare. One of good candidate methods is to use commonly available cameras and extract information that can help to estimate heart rate from a human face. Generally, such information can be retrieved using two different approaches: photoplethysmography (PPG) and ballistocardiography (BCG). PPG exploits slight color changes caused by blood volume variations during heartbeats; thus, it tends to be vulnerable to unstable lighting conditions. BCG exploits subtle head motions caused by pumped blood travelling through the carotid artery during heartbeats; thus, it is vulnerable to the voluntary head movements that are not related to heartbeats. Nevertheless, most related works use either to estimate the heart rate. In this paper, we propose to combine two approaches to be robust to challenging conditions. Specifically, we explore possible ways to combine raw signals obtained from two approaches and verify that the proposed combination shows better accuracies under challenging conditions, such as voluntary head movements and ambient lighting changes.