• 한국통신학회논문지
• /
• 제34권10A호
• /
• pp.769-775
• /
• 2009

본 논문에서는 선박용 해양텔레매틱스(Maritime Telematics)를 위한 무선 PAN(WPAN: Wireless Personal Area Network) 시스템의 에너지 효율성을 결정하는 전송파라미터를 분석하여 최적의 전송파라미터 선택 방안을 제시한다. 본 논문에서 고려하는 WPAN 시스템은 IEEE 802.15.3의 TDMA 기반의 HR(high rate)-WPAN으로 선박네트워크에 적용할 경우에 고려되어야하는 전송 파라미터를 분석하고 에너지를 절약할 수 있는 전송파라미터 결정방식에 대하여 제안한다. 특히 선박환경에서 에너지 소모량과 밀접한 채널 파라미터인 경로손실(path loss)을 결정하는 선박의 구성재질(철골구조의 대형선박, FRP소재의 중소형선박)과 HR-WPAN 전송파라미터들에 따른 에너지 소모량을 분석하고 에너지 효율성을 극대화할 수 있는 선택방안을 제시한다. 시뮬레이션 결과 각 선박환경에 따라 전송률 선택방식, 전송전력 조절 방식 및 데이터 분할(fragment)크기의 선택에 따라 에너지 효율성능이 결정됨을 보였고 최적의 선택방안을 제시하였다.

• Journal of Power Electronics
• /
• 제18권2호
• /
• pp.332-342
• /
• 2018

This paper proposes a parameter design method for multi-element resonant converters (MERCs) with a unique resonant zero point (RZP). This method is mainly composed of four steps. These steps include program filtration, loss comparison, 3D figure fine-tuning and priority compromise. It features easy implementation, effectiveness and universal applicability for almost all of the existing RZP-MERCs. Meanwhile, other design methods are always exclusive for a specific topology. In addition, a novel dual-CTL converter is also proposed here. It belongs to the RZP-MERC family and is designed in detail to explain the process of parameter selection. The performance of the proposed method is verified experimentally on a 500W prototype. The obtained results indicate that with the selected parameters, an extensive dc voltage gain is obtained. It also possesses over-current protection and minimal switching loss. The designed converter achieves high efficiencies among wide load ranges, and the peak efficiency reaches 96.9%.

• Journal of Information Processing Systems
• /
• 제13권5호
• /
• pp.1168-1182
• /
• 2017

In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.

• Quantitative Bio-Science
• /
• 제37권2호
• /
• pp.133-141
• /
• 2018

Graphical lasso is one of the most popular methods to estimate a sparse precision matrix, which is an inverse of a covariance matrix. The objective function of graphical lasso imposes an ${\ell}_1$-penalty on the (vectorized) precision matrix, where a tuning parameter controls the strength of the penalization. The selection of the tuning parameter is practically and theoretically important since the performance of the estimation depends on an appropriate choice of tuning parameter. While information criteria (e.g. AIC, BIC, or extended BIC) have been widely used, they require an asymptotically unbiased estimator to select optimal tuning parameter. Thus, the biasedness of the ${\ell}_1$-regularized estimate in the graphical lasso may lead to a suboptimal tuning. In this paper, we propose a two-staged bias-correction procedure for the graphical lasso, where the first stage runs the usual graphical lasso and the second stage reruns the procedure with an additional constraint that zero estimates at the first stage remain zero. Our simulation and real data example show that the proposed bias correction improved on both edge recovery and estimation error compared to the single-staged graphical lasso.

• Communications for Statistical Applications and Methods
• /
• 제24권6호
• /
• pp.673-683
• /
• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.1459-1464
• /
• 1996

The most important parameter for hydraulic active suspension system is to sustain desirable vehicle maneuvering stability and ride comfort without increasing consumption power. The performance of hydraulic active suspension system depends on damping force of body damping valve and piston damping valve. Hydraulic actuator design and damping valve parameter selection are essential and basic procedure to design hydraulic active suspension system. This paper is on computer simulation with use of mathematical model that was delivered from dynamic characteristic of hydraulic actuator, as know basic damping characteristics of hydraulic active suspension system. The aim of this paper is to select the system parameter that affect mainly hydraulic active suspension, and identify the validity on the system parameter selection.

• 대한기계학회논문집A
• /
• 제26권4호
• /
• pp.683-692
• /
• 2002

The most important parameter for hydraulic active suspension system is to sustain desirable vehicle maneuvering stability and ride comfort without increasing power consumption. The performance of hydraulic active suspension system depends on damping force of body damping valve and piston damping valve. Hydraulic actuator design and damping valve parameter selection are essential and basic procedure to design hydraulic system. This paper is on computer simulation with use of mathematical model that was delivered from dynamic characteristic of hydraulic actuator, as know basic damping characteristics of hydraulic active suspension system. The aim of this paper is to select the system parameter that affect mainly hydraulic active suspension, and identify the validity on the system parameter selection.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
• /
• pp.264-267
• /
• 2002

This paper proposed a effective priority selection algorithm named weighted round-robin algorithm and show the implementation result of DMAC priority selection module using prosed weighted round-robin algorithm. I parameterize timing constraints of each functional module, which decide the effectiveness of system. Proposed weighted round-robin algorithm decide the most effective module for data transmission using parameterize timing constraints and update timing parameter of each module for next transmission module selection. I implement DMAC priority selection module using this weighted round-robin algorithm and can improve the timing effective for data transmission from memory to functional module or one functional module to another functional module.

• Journal of Communications and Networks
• /
• 제14권4호
• /
• pp.434-442
• /
• 2012

Several gateway selection schemes have been proposed that select gateway nodes based on a single Quality of Service (QoS) path parameter, for instance path availability period, link capacity or end-to-end delay, etc. or on multiple non-QoS parameters, for instance the combination of gateway node speed, residual energy, and number of hops, for Mobile Ad hoc NETworks (MANETs). Each scheme just focuses on the ment of improve only a single network performance, i.e., network throughput, packet delivery ratio, end-to-end delay, or packet drop ratio. However, none of these schemes improves the overall network performance because they focus on a single QoS path parameter or on set of non-QoS parameters. To improve the overall network performance, it is necessary to select a gateway with stable path, a path with themaximum residual load capacity and the minimum latency. In this paper, we propose a gateway selection scheme that considers multiple QoS path parameters such as path availability period, available capacity and latency, to select a potential gateway node. We improve the path availability computation accuracy, we introduce a feedback system to updated path dynamics to the traffic source node and we propose an efficient method to propagate QoS parameters in our scheme. Computer simulations show that our gateway selection scheme improves throughput and packet delivery ratio with less per node energy consumption. It also improves the end-to-end delay compared to single QoS path parameter gateway selection schemes. In addition, we simulate the proposed scheme by considering weighting factors to gateway selection parameters and results show that the weighting factors improve the throughput and end-to-end delay compared to the conventional schemes.

• 대한의용생체공학회:의공학회지
• /
• 제42권6호
• /
• pp.268-276
• /
• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.