• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.61-69
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• 2009

Most existing clustering protocols have been aimed to provide balancing the residual energy of each node and maximizing life-time of wireless sensor networks. In this paper, we present the threshold based cluster head replacement strategy for clustering protocols in wireless sensor networks. This protocol minimizes the number of cluster head selection by preventing the cluster head replacement up to the threshold of residual energy. Reducing the amount of head selection and replacement cost, the life-time of the entire networks can be extended compared with the existing clustering protocols. Our simulation results show that our protocol outperformed than LEACH in terms of balancing energy consumption and network life-time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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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.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.49-56
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• 2010

Severe energy constraints and the low power consumption require the significance of the energy efficient error control mechanisms in wireless sensor network (WSN). In this paper, an automatic repeat request (ARQ) methodology for the analysis of error control schemes in WSN is presented such that the effects of packet length, the modulation scheme and the interference effect of the wireless channel are investigated. Moreover, an analyis of ARQ error control is provided by considering two major architectures for wireless sensor network, i.e., Mica2 and MicaZ sensor nodes. And the throughput performance of WSN with asynchronous FSK signal and DSSS-OQPSK signal with selective repeat ARQ scheme are analyzed in multiple interference environment, and the probability of receiving a correct bit and packet from target node to sink node is evaluated as a function of the channel parameter, the number of wireless sensor node, and the spreading factor.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.4
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• pp.27-32
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• 2007

Gears, crucial components in modern precision machinery for power transmission mechanisms, are required to have low contacting noise with high torque transmission, which makes the use of gear-tooth profile modifications and gear-tooth surface crowning extremely efficient and valuable. Due to the shortcomings of current techniques, such as manual rectification, mechanical modification, and numerically controlled rectification, we propose a novel electrochemical gear-tooth profile modification method based on an artificial neural network control technique. The fundamentals of electrochemical tooth-profile modifications based on real-time control and a mathematical model of the process are discussed in detail. Due to the complex and uncertain relationships among the machining parameters of electrochemical tooth-profile modification processes, we used an artificial neural network to determine the required processing electric current as the tooth-profile modification requirements were supplied. The system was implemented and a practical example was used to demonstrate that this technology is feasible and has potential applications in the production of precision machinery.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2441-2446
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• 2009

Wireless Sensor Network collects a data from the specific area and the control is composed of small sensor nodes. Like this sensors to after that is established at the beginning are operated with the battery, the operational duration until several years must be continued from several months and will be able to apply the resources which is restricted in efficiently there must be. In this paper RHC (rounting history cache) applies in Directed Diffusion which apply a data central concept a reliability and an efficiency in data transfer course set. RHC algorithms which proposes each sensor node updated RHC of oneself with periodic and because storing the optimization course the course and, every event occurrence hour they reset the energy is wasted the fact that a reliability with minimization of duplication message improved.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.191-197
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• 2003

Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, such as inter alia low impedance faults (LIF) and high impedance faults (HIF). The latter in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if undetected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. Because of the randomness and asymmetric characteristics of HIFs, their modeling is difficult and numerous papers relating to various HIF models have been published. In this paper, the model of HIFs in transmission lines is accomplished using the characteristics of a ZnO arrester, which is then implemented within the overall transmission system model based on the electromagnetic transients program (EMTP). This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System (ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square (RMS) values of 3-phase currents and zero sequence current. The performance of the proposed algorithm is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results demonstrate that the ANFIS can detect and classify faults including LIFs and HIFs accurately within half a cycle.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.154-158
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• 2008

Autonomous business card exchange system using ZigBee with low power and short range was configured In the autonomous business card exchange system characterized as full mesh network in which every node exchanges each information one by one, it is necessary to reduce the time taken for information to be exchanged. In this paper, the novel method where the node ID is exchanged based on CSMA/CA and then the information of each node is broadcast to other nodes according to the ID list based on FIFO. The time required for exchanging information using the proposed method was analyzed and compared with the direct exchange method based on CSMA/CA. The results show that it takes less time in the proposed method than the direct exchange time.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.4
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• pp.82-90
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• 2009

Pneumatic cylinder is one of the low cost actuation sources which have been applied in industrial and prosthetic application since it has a high power/weight ratio, a high-tension force and a long durability However, the control problems of pneumatic systems, oscillatory motion and compliance, have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, one solution for position control of a robot arm, which is driven by two pneumatic artificial muscles, is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load of the robot arm. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. This estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3594-3610
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• 2015

Recent advancements in broadband wireless communication technologies enable mobile users to receive video streaming services with various smart devices. The long term evolution (LTE) network provides high bandwidth and low latency for several emerging mobile applications. This paper proposes the buffer aware scheduling (BAS) approach to schedule the downlink video traffic in LTE network. The proposed BAS scheme applies the weighting function to heuristically adjust the scheduling priority by considering the buffer status and channel condition of UE so as to reduce the time that UE stays in the connected state without receiving data. Both of 1080P and 2160P resolution video streaming sources were applied for exhaustive simulations to examine the performance of the proposed scheme by comparing to that of the fair bandwidth (FB) and the best channel quality indicator (CQI) schemes. The simulation results indicate that the proposed BAS scheme not only achieves better performance in power saving, streaming delivery time, and throughput than the FB scheme while maintaining the similar performance as the best CQI scheme in light traffic load. Specifically, the proposed scheme reduces streaming delivery time and generates less signaling overhead than the best CQI scheme when the traffic load is heavy.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2678-2685
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• 2020

The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.