• 한국통신학회논문지
• /
• 제22권5호
• /
• pp.1075-1082
• /
• 1997

In this paper, we propose a channel assignment scheme for reducing call blocking rate in a base station(BS) of DS-CDMA cellular systems. The proposed scheme can e applied to the case where the capacity of reverse radio link is enough, but not are the available traffic channels performing the digital modulation and demodulation functions between a mobile station and the base station. The proposed scheme takes advantage of the feature of soft handoff in which a mobile station keeps its communication link even if one of the two communication links is released. The scheme estimates the mean and variance of the received power level measured at the base station before assigning a traffic channel for a new call request. The BS makes decision based on the estimated balues whether the new call request will be accepted or not. If it is decided that the capacity of reverse radio link is enough, but all traffic channels are not available, then the BS increases the soft handoff parameter T_DROP to release the traffic channels of mobile stations loactedin soft handoff area. The BS assigns the released traffic channel to anew call or a handoff call. The performance of the proposed channel assignment scheme is evaluated by computer simulation. The results show that the call blocking rate for new calls and handoff calls is reduced.

• Journal of Power Electronics
• /
• 제12권5호
• /
• pp.787-799
• /
• 2012

Real and reactive power flows on a transmission line interact inherently. This situation degrades power flow controller performance when independent real and reactive power flow regulation is required. In this study, a quasi multi-pulse interline power flow controller (IPFC), consisting of eight six-pulse voltage source converters (VSC) switched at the fundamental frequency is proposed to control real and reactive power flows dynamically on a transmission line in response to a sequence of set-point changes formed by unit-step reference values. It is shown that the proposed hybrid fuzzy-PI commanded IPFC shows better decoupling performance than the parameter optimized PI controllers with analytically calculated feed-forward gains for decoupling. Comparative simulation studies are carried out on a 4-machine 4-bus test power system through a number of case studies. While only the fuzzy inference of the proposed control scheme has been modeled in MATLAB, the power system, converter power circuit, control and calculation blocks have been simulated in PSCAD/EMTDC by interfacing these two packages on-line.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1996년도 하계학술대회 논문집 B
• /
• pp.797-799
• /
• 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.

• 한국군사과학기술학회지
• /
• 제13권5호
• /
• pp.894-902
• /
• 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
• /
• 제17권3호
• /
• pp.744-755
• /
• 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.

• 한국항행학회논문지
• /
• 제14권6호
• /
• pp.874-881
• /
• 2010

무인기의 자동 비행 제어 시스템은 기체의 형태, 크기, 무게 등의 정적 및 동적 변화에 따라 스스로 비행계수를 조정하여 목표 비행궤적을 정확히 따라가도록 제어할 필요가 있다. 본 논문에서는 PID 제어 기법을 이용하는 비행제어시스템에 기계학습모듈(MLM)을 추가하여 기체의 특성 변화에 따라 제어계수를 비행중 실시간 자동으로 조정하는 시스템을 제안한다. MLM은 선형회귀분석과 보정학습을 이용하여 설계되었으며 MLM을 통해 학습된 제어계수의 적합성을 평가하는 평가모듈(EvM)을 함께 모델링 하였다. 이 시스템은 FDC 비버 시뮬레이터를 기반으로 실험하였으며 그 결과를 분석 제시하였다.

• 한국통신학회논문지
• /
• 제16권1호
• /
• pp.35-45
• /
• 1991

본 논문에서는 Backpropagation 학습 이론을 사용한 다층 구조 신경 회로망을 이용하여 3차원적으로 왜곡된 항공기 인식과 항공기의 3차원 회전 방향 추정을 컴퓨터 시뮬레이션을 통하여 수행하였다. 항공기 영상으로 부터 2차원 영상에서 왜곡 불변 (distortion invariant)특정을 가지는 피치 $(L,\;{\Phi})$를 추출하여 신경 회로망 항공기 인식기의 학습(training)에 사용하였다. 그리고 신경 회로망 인식기 설계시 그 구조를 최적화 함으로써 높은 인식률을 가지는 항공기 인식기를 구성하였다. 신경 회로망 학습 과정에서 학습 이론으로는 변형된 backpropagation 학습 이론을 도입하고 아울러 학습 수행중에 학습 변수(learning parameter)값을 변화 시키는 방법을 사용하여 전체 학습 시간을 효과적으로 단축시킬 수 있었다.

• Advances in aircraft and spacecraft science
• /
• 제5권3호
• /
• pp.295-318
• /
• 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).

• 대한원격탐사학회지
• /
• 제1권1호
• /
• pp.5-27
• /
• 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
• /
• 제44권4호
• /
• pp.613-623
• /
• 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.