• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.797-799
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• 1996

This paper presents a simple method for evaluating of voltage stability using the line flow equation. Line flow equations($P_{ij}$, $Q_{ij}$) are comprised of state variable, $V_i$, ${\delta}_i$, $V_j$ and ${\delta}_j$, and line parameter, r and x. Using the feature of polar coordinate, these equations become one equation with two variables, $V_i$ and $V_j$. Moreover, if bus j is slack bus or generator bus, which is specified voltage magnitude, it becomes One equation with one variable $V_i$, that is, may be formulated with the second-order equation for $V_i^2$. Therefore, solutions are obtained with simple computation. Solutions obtained are used for evaluating of voltage stability through sensitivity analysis. Also, considering of reactive power source, method for evaluating the voltage stability is introduced. The proposed method was validated to 2-bus and IEEE 6-bus system.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.894-902
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• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.

• Journal of Power Electronics
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• v.17 no.3
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• pp.744-755
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• 2017

With the high penetration of various sustainable energy sources, the control and protection of Microgrids has become a challenging problem considering the inherent current limitation feature of inverter-based Distributed Generators (DGs) and the bidirectional power flow in Microgrids. In this paper, a hybrid control and protection scheme is proposed, which combines the traditional inverse-time overcurrent protection with the biased differential protection for different feeders with different kinds of loads. It naturally accommodates various control strategies such as P-Q control and V-f control. The parameter settings of the protection scheme are analyzed and calculated through a fast Fourier transform algorithm, and the stability of the control strategy is discussed by building a small signal model in MATLAB. Different operation modes such as the grid-connected mode, the islanding mode, and the transitions between these two modes are ensured. A Microgrid model is established in PSCAD and the analysis results show that a Microgrid system can be effectively protected against different faults such as the single phase to ground and the three phase faults in both the grid-connected and islanded operation modes.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.874-881
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• 2010

A automatic flight control system(AFCS) of UAS needs to control its flight path along target path exactly as adjusts flight coefficient itself depending on static or dynamic changes of airplane's features such as type, size or weight. In this paper, we propose system which tunes control gain autonomously depending on change of airplane's feature in flight as adding MLM(Machine Learning Module) on AFCS. MLM is designed with Linear Regression algorithm and Reinforcement Learning and it includes EvM(Evaluation Module) which evaluates learned control gain from MLM and verified system. This system is tested on beaver FDC simulator and we present its analysed result.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.1
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• pp.35-45
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• 1991

Multi layer neural network using backpropagation learning algorithm is used to achieve identification and orientation estimation of different classes of aircraft in the variety of 3-D orientations. In-plane distortion invarient$(L,\;{\Phi})$ feature was extracted from each aircraft image to be used for training neural network aircraft classifier. For aircraft identification the optimum structure of the neural network classifier is studied to obtain high classification performance. Effective reductioin of learning time was achieved by using modified backpropagation learning algorithm and varying, learning parameters.

• Advances in aircraft and spacecraft science
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• v.5 no.3
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• pp.295-318
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• 2018

This research studies thermo-elastic behavior of rotating micro discs that are employed in various micro devices such as micro gas turbines. It is assumed that material is functionally graded with a variable profile thickness, density, shear modulus and thermal expansion in terms of radius of micro disc and as a power law function. Boundary condition is considered fixed-free with uniform thermal loading and elastic field is symmetric. Using incompressible material's constitutive equation, we extract governing differential equation of four orders; to solution this equation, we utilize general differential quadrature (GDQ) method and the results are schematically pictured. The obtained result in a particular case is compared with another work and coincidence of results is shown. We will find out that surface effect tends to split micro disc's area to compressive and tensile while nonlocal parameter tries to converge different behaviors with each other; this convergence feature make FGIMs capable to resist in high temperature and so in terms of thermo-elastic behavior we can suggest, using FGIMs in micro devices such as micro turbines (under glass transition temperature).

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.363-364
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• 2009

방송 산업이 기술적 구조적으로 발전하고 시청자의 수준 향상 및 문화 산업이 급변함에 따라 현대사회에서 방송 분야는 거대 성장을 거듭하고 있다. 이러한 방송 산업의 시대적 변화속에서 지속적으로 관심의 대상이 되고 있는 것이 시청자들의 수준 및 변화의 초점이며 이를 파악하여 원활한 방송의 진행을 주도해야 하는 것이 방송 진행자의 역할이다. 따라서 본 논문에서는 충북지역의 방송 3사에서 뉴스를 담당하고 있는 진행자에 대한 음성을 수집하여 다양한 음성 분석 요소들을 적용하고 이에 따른 결과값을 기반으로 방송 진행자의 음성에 대한 특징적 정보를 추출하는 실험을 수행하였다. 특히, 음성을 통해 전달할 수 있는 영향력을 분석하기 위해 피치, 지터, 짐머, 안정도, 및 스펙트로그램 등의 다양한 음성 분석 요소를 적용하였으며 결과값에 대한 비교, 분석을 수행하였다.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3359-3366
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• 2023

In this study, a spiking cortical model (SCM) based n-g discrimination method is proposed. The SCM-based algorithm is compared with three other methods, namely: (i) the pulse-coupled neural network (PCNN), (ii) the charge comparison, and (iii) the zero-crossing. The objective evaluation criteria used for the comparison are the FoM-value and the time consumption of discrimination. Experimental results demonstrated that our proposed method outperforms the other methods significantly with the highest FoM-value. Specifically, the proposed method exhibits a 34.81% improvement compared with the PCNN, a 50.29% improvement compared with the charge comparison, and a 110.02% improvement compared with the zero-crossing. Additionally, the proposed method features the second-fastest discrimination time, where it is 75.67% faster than the PCNN, 70.65% faster than the charge comparison and 38.4% slower than the zero-crossing. Our study also discusses the role and change pattern of each parameter of the SCM to guide the selection process. It concludes that the SCM's outstanding ability to recognize the dynamic information in the pulse signal, improved accuracy when compared to the PCNN, and better computational complexity enables the SCM to exhibit excellent n-γ discrimination performance while consuming less time.