• 드라이브 ㆍ 컨트롤
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• 제12권3호
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• pp.18-24
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• 2015

The plug-in hybrid electric vehicle has a high fuel economy and can be driven long distances. Its different modes include the electric vehicle, hybrid electric vehicle, and only engine operating mode. A power management strategy is important to determine which mode should be selected. The strategy makes the vehicle more efficient using appropriate power sources for driving. However, the strategy usually needs a driving speed profile which is future driving cycle. If the profile is known, the strategy easily determines which mode is driven efficiently. However, it is difficult to estimate the speed profile for a real system. To address this problem, this paper proposes a new power distribution strategy using a neural network. The average speed and driving range are used as input parameters to train the neural network system. The strategy determines a limit for the use of the battery and the desired power is distributed between the engine and the motor simultaneously. Its fuel economy can increase by improving the basic strategy.

• 산업공학
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• 제4권1호
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• pp.83-91
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• 1991

This study proposes a half-simulation-half-real system that is used as an FMS building tool. In this hybrid system. softwares related to the operation of the FMS and the computer network on which the softwares run are real, while the physical movements of the devices in the system are simulated. The study shows the structure of the proposed system as well as the building procedure for the system. Adventages and usages of the system are also stated in detail.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전력기술부문
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• pp.162-164
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• 2005

This paper presents an hour-ahead System Marginal Price (SMP) forecasting framework based on a neural network. Recently, the deregulation in power industries has impacted on the power system operational problems. The bidding strategy of market participants in energy market is highly dependent on the short-term price levels. Therefore, short-term SMP forecasting is a very important issue to market participants to maximize their profits. and to market operator who may wish to operate the electricity market in a stable sense. The proposed hybrid neural network is composed of tow parts. First part of this scheme is pattern classification to input data using Kohonen Self-Organizing Map (SOM) and the second part is SMP forecasting using back-propagation neural network that has three layers. This paper compares the forecasting results using classified input data and unclassified input data. The proposed technique is trained, validated and tested with historical date of Korea Power Exchange (KPX) in 2002.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.3152-3171
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• 2022

This work investigates the physical layer secrecy of a multi-antenna hybrid satellite-terrestrial relay networks (HSTRN) with jamming, in which a satellite aims to make communication with a destination user by means of a relay, along with spatially random eavesdroppers. In order to weaken the signals of eavesdroppers, the conventional relay can also generate intentional interference, besides forwarding the received signal. Shadowed-Rician fading is adopted in satellite link, while Rayleigh fading is adopted in terrestrial link, eavesdropper link and jamming link. The analytical and asymptotic formulas for the system secrecy outage probability (SOP) are characterized. Practical insights on the diversity order of the network are revealed according to the asymptotic behavior of SOP at high signal-to-noise ratio (SNR) regime. Then, analysis of the system throughput is examined to assess the secrecy performance. In the end, numerical simulation results are presented to validate the theoretical analysis and point out: (1) The secrecy performance of the considered network is affected by the channel fading scenario, the system configuration; (2) Decrease of the relay coverage airspace can provide better SOP performance; (3) Jamming from the relay can improve secrecy performance without additional network resources.

• Asia pacific journal of information systems
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• 제9권4호
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• pp.23-40
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• 1999

This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining neural network and rough set approach, We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables and objects (i.e., firms) is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. Through the reduction of information table, it is expected that the performance of the neural network improve. The rules developed by rough sets show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2,400 Korean firms during the period 1994-1996 were selected, and for the validation, k-fold validation was used.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1292-1298
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• 2017

In this paper, we represent a nonlinear identification of electronic brake pedal behavior in the brake-by-wire (BBW) system based on hybrid group method of data handling (GMDH) and genetic algorithm (GA). A GMDH is a kind of multi-layer network with a structure that is determined through training and which can express nonlinear dynamics as a mathematical model. The GA is used in the GMDH, enabling each neuron to search for its optimal set of connections with the preceding layer. The results obtained with this hybrid approach were compared with different nonlinear system identification methods. The experimental results showed that the hybrid approach performs better than the other methods in terms of root mean square error (RMSE) and correlation coefficients. The hybrid GMDH/GA approach was effective for modeling and predicting the brake pedal system under random braking conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1520-1542
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• 2020

Traditional software testing typically uses many physical resources to manually build various test environments, resulting in high resource costs and long test time due to limited resources, especially for small enterprises. Cloud computing can provide sufficient low-cost virtual resources to alleviate these problems through the virtualization of physical resources. However, the provision of various test environments and services for implementing software testing rapidly and conveniently based on cloud computing is challenging. This paper proposes a multilayer cloud testing model based on cloud computing and implements a hybrid cloud testing system based on virtual machines (VMs) and networks. This system realizes the automatic and rapid creation of test environments and the remote use of test tools and test services. We conduct experiments on this system and evaluate its applicability in terms of the VM provision time, VM performance and virtual network performance. The experimental results demonstrate that the performance of the VMs and virtual networks is satisfactory and that this system can improve the test efficiency and reduce test costs through rapid virtual resource provision and convenient test services.

• 한국IT서비스학회지
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• 제8권3호
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• pp.171-184
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• 2009

Since the development of the Distributed Hash Tables (DHTs), the distributed lookup services are one of the hot topics in the networking area. The main reason of this popularity is the simplicity of the lookup structure. However, the simple key based search mechanism makes the so called "keyword" based search difficult if not impossible. Thus, the applicability of the DHTs is limited to certain areas. In this paper. we find that DHTs can be used as the ubiquitous sensor network (USN) metadata lookup service across a large number of sensor networks. The popularity of the Ubiquitous Sensor Network has motivated the development of the USN middleware services for the sensor networks. One of the key functionalities of the USN middleware service is the lookup of the USN metadata, by which users get various information about the sensor network such as the type of the sensor networks and/or nodes, the residual of the batteries, the type of the sensor nodes. Traditional distributed hash table based lookup systems are good for one sensor network. However, as the number of sensor network increases, the need to integrate the lookup services of many autonomous sensor networks so that they can provide the users an integrated view of the entire sensor network. In this paper, we provide a hybrid lookup model, in which the autonomous lookup services are combined together and provide seamless services across the boundary of a single lookup services. We show that the hybrid model can provide far better lookup performance than a single lookup system.

• IEIE Transactions on Smart Processing and Computing
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• 제1권1호
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• pp.27-33
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• 2012

The Ethernet Passive Optical Network (EPON) has been considered to be one of the most promising solutions for the implementation of the Fiber To The Home (FTTH) technology designed to ameliorate the "last mile" bandwidth bottleneck. In the EPON network, an efficient and fair bandwidth allocation is a very important issue, since multiple optical network units (ONUs) share a common upstream channel for packet transmission. To increase bandwidth utilization, an EPON system must provide a way to adaptively allocate the upstream bandwidth among multiple ONUs in accordance to their bandwidth demands and requirements. We present a new hybrid method that satisfies these requirements. The advantage of our method comes from the consideration of application-specific bandwidth allocation and the minimization of the idle bandwidth. Our simulation results show that our proposed method outperforms existing dynamic bandwidth allocation methods in terms of bandwidth utilization.

• Asia pacific journal of information systems
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• 제5권1호
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• pp.36-51
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• 1995

본 논문에서는 비구조적인 의사결정문제를 효과적으로 해결하기 위하여 감독학습 인공신경망 모형과 비감독학습 인공신경망 모형을 결합한 하이브리드 인공신경망 모형인 HYNEN(HYbrid NEural Network) 모형을 제안한다. HYNEN모형은 주어진 자료를 클러스터화 하는 CNN(Clustering Neural Network)과 최종적인 출력을 제공하는 ONN(Output Neural Network)의 2단계로 구성되어 있다. 먼저 CNN에서는 주어진 자료로부터 적정한 퍼지규칙을 찾기 위하여 클러스터를 구성한다. 그리고 이러한 클러스터를 지식베이스로하여 ONN에서 최종적인 의사결정을 한다. CNN에서는 SOFM(Self Organizing Feature Map)과 LVQ(Learning Vector Quantization)를 클러스터를 만든 후 역전파학습 인공신경망 모형으로 이를 학습한다. ONN에서는 역전파학습 인공신경망 모형을 이용하여 각 클러스터의 내용을 학습한다. 제안된 HYNEN 모형을 우리나라 기업의 도산자료에 적용하여 그 결과를 다변량 판별분석법(MDA:Multivariate Discriminant Analysis)과 ACLS(Analog Concept Learning System) 퍼지 ARTMAP 그리고 기존의 역전파학습 인공신경망에 의한 실험결과와 비교하였다.