• Journal of Computing Science and Engineering
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• 제9권2호
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• pp.108-117
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• 2015

Interrupt handling is generally separated from process scheduling. This can lead to a scheduling anomaly and priority inversion. The processor can interrupt a higher priority process that is currently executing, in order to handle a network packet reception interruption on behalf of its intended lower priority receiver process. We propose a new network interrupt handling scheme that combines interrupt handling with process scheduling and the priority of the process. The proposed scheme employs techniques to identify the intended receiver process of an incoming packet at an earlier phase. We implement a prototype system of the proposed scheme on Linux 2.6, and our experiment results show that the prototype system supports the predictable real-time behavior of higher priority processes even when excessive traffic is sent to lower priority processes.

• 조명전기설비학회논문지
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• 제27권10호
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• pp.50-58
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• 2013

In Wireless Sensor Network, clustering scheme is used to prolong the lifetime of WSN by efficient usage of energy of sensor. In the distributed clustering protocol just like LEACH, every sensor in a network plays a cluster head role once during each epoch. So the FND is prolonged. But, even though every sensor plays a head role, the energy consumed by each sensor is different because the energy consumed increases according to the distance to the Base Station by the way of multiple increase. In this paper, we propose a mechanism to select a head depending on the distance to Base Station, which extends the timing of FND occurrence by 68% compared to the LEACH and makes network stable.

• 전기학회논문지P
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• 제54권1호
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• pp.20-29
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• 2005

This paper presents an implementation of efficiency optimization of reluctance synchronous motor (RSM) using a neural network (NN) with a direct torque control (DTC). The equipment circuit considered with iron losses in RSM is analyzed theoretically, and the optimal current ratio between torque current and exiting current component are derived analytically. For the RSM driver, torque dynamic can be maintained with DTC using TMS320F2812 DSP Controller even with controlling the flux level because a torque is directly proportional to the stator current unlike induction motor. In order to drive RSM at maximum efficiency and good dynamics response, the Backpropagation Neural Network is adapted. The experimental results are presented to validate the applicability of the proposed method. The developed control system show high efficiency and good dynamic response features with 1.0 [kW] RSM having 2.57 inductance ratio of d/q.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.151-155
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• 1996

To automate an excavator the control issues resulting from environmental uncertainties must be solved. In particular the interactions between the excavation tool and the excavation environment are dynamic, unstructured and complex. In addition, operating modes of an excavator depend on working conditions, which makes it difficult to derive the exact mathematical model of excavator. Even after the exact mathematical model is established, it is difficult to design of a controller because the system equations are highly nonlinear and the state variable are coupled. The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbance and performance improvement with the on-line learning in the position control of excavator attachment.

• 제어로봇시스템학회논문지
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• 제14권4호
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• pp.399-405
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• 2008

Recently, robot technology is actively going on progress to the field of various services such as home care, medical care, entertainment, and etc. Because these service robots are in use nearby person, they need to be operated safely even though hardware and software faults occur. This paper proposes a Fault-Tolerant middleware for a robot system, which has following two characteristics: supporting of heterogeneous network interface and processing of software components and network faults. The Fault-Tolerant middleware consists of a Service Layer(SL), a Network Adaptation Layer(NAL), a Network Interface Layer(NIL), a Operating System ion Layer(OSAL), and a Fault-Tolerant Manager(FTM). Especially, the Fault-Tolerant Manager consists of 4 components: Monitor, Fault Detector, Fault Notifier, and Fault Recover to detect and recover the faults effectively. This paper implements and tests the proposed middleware. Some experiment results show that the proposed Fault-Tolerant middleware is working well.

• 전자공학회논문지B
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• 제32B권3호
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• pp.48-56
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• 1995

This paper presents a method of implementing efficient optical flow estimation for dynamic scene analysis using the hierarchical Hopfield neural networks. Given the two consequent inages, Zhou and Chellappa suggested the Hopfield neural network for computing the optical flow. The major problem of this algorithm is that Zhou and Chellappa's network accompanies self-feedback term, which forces them to check the energy change every iteration and only to accept the case where the lower the energy level is guaranteed. This is not only undesirable but also inefficient in implementing the Hopfield network. The another problem is that this model cannot allow the exact computation of optical flow in the case that the disparities of the moving objects are large. This paper improves the Zhou and Chellapa's problems by modifying the structure of the network to satisfy the convergence condition of the Hopfield model and suggesting the hierarchical algorithm, which enables the computation of the optical flow using the hierarchical structure even in the presence of large disparities.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.213-217
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• 1990

Neural networks are a class of systems that have many simple processors (neurons) which are highly interconnected. The function of each neuron is simple, and the behavior is determined predominately by the set of interconnections. Thus, a neural network is a special form of parallel computer. Although a major impetus for using neural networks is that they may be able to "learn" the solution to the problem that they are to solve, we argue that another, perhaps even stronger, impetus is that they provide a framework for designing massively parallel machines. The highly interconnected architecture of switching networks suggests similarities to neural networks. Here, we present two switching applications in which neural networks can solve the problems efficiently. We also show that a computational advantage can be gained by using nonuniform time delays in the network.e network.

• 전자공학회논문지S
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• 제35S권1호
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• pp.80-87
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• 1998

In this study, a multi-element ultrasound transducer has been developed aiming at basic experiment of three-dimension endovascular ultrasound endscopy for clinical diagnos, and experimental results of two-dimensional object imaging in the water are presented by the ultrasound tranducer and neural network. Each ultrasound echo received by thirty-six angular transducer elements is inputed to the eural network, and then backpropagation is used as a learning algorithm. A three-layer artificial neural network is used for learning and imaging of targetw placed in front of the transducer. The object shape of imaging is restricted to rectangular shapes by considering experimental restraint conditions. As a result, rough visualization can be realized even for objects with unlearned shapes through the training by primitive patterns of a various sized rectangular targets.

• 전기전자학회논문지
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• 제18권4호
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• pp.517-522
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• 2014

본 논문은 임의의 네트워크 시간 지연이 존재하는 선형 다개체 시스템의 일치문제를 다룬다. 다개체 시스템의 상태일치를 위한 충분조건은 선형행렬방정식을 이용하여 제공된다. 또한, 제안한 충분조건아래에서 임의의 크기를 갖는 네트워크 지연이 존재하는 경우에도 일치에 도달할 수 있음을 증명한다. 마지막으로 제안한 결과의 유효성을 증명하기 위하여 수치 예제를 제공한다.

• 한국멀티미디어학회논문지
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• 제20권11호
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• pp.1776-1784
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• 2017

There are two types of WSN(wireless sensor networks) in terms of sensor node's capability, that is, homogeneous or heterogeneous WSN. Even though the latter has better performance than the former, it requires some overhead for deploying nodes or clustering the network. In this paper, we propose a new scheme, called VHS(Virtual Heterogenous Sensor-Network), which uses a homogeneous WSN regarding energy in a heterogeneous way. The proposed scheme's performance has been evaluated and compared with other homogeneous schemes by simulation. The results are shown to be better than the other existing homogeneous schemes used in a sample sensor network application.