• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.89-98
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• 2012

In this paper, we proposed the efficient addressing scheme for improving the performance of routing algorithm by using ZigBee in Smart Grid environment. In a 16-bit address space and the network size of a few thousands, it is very unlikely to suffer from frequent address collisions. In response, we propose an elegant (x, y, z) coordinate axes addressing scheme from divided address space of 16 bit and its routing algorithm. One of disadvantages of (x, y) coordinate axes addressing, however, is that any router may not hold as many children as proposed, since sensor nodes tend to be connected to a geographically nearby router. We also present an adaptive routing algorithm for location-aware routing algorithms, using our addressing scheme. As a result, each node was reduced not only bitwise but also multi hop using the coordinate axes while routing and the effective address assignment and routing is to minimize the average energy consumption of each node in the network.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.41-48
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• 2012

A Vehicular Ad-hoc Network (VANET) is a vehicular specific type of a mobile ad-hoc network, to provide temporary communications among nearby vehicles. Mobile node of VANET consumes energy and resource with participating in the member of network. In a VANET, data replication and cooperative caching have been used as promising solutions to improve system performance. Existing cooperative caching scheme in a VANET mostly focuses on weak consistency is not always satisfactory. In this paper, we propose an efficient cache management scheme to maintain strong data consistency in a VANET. We make an adaptive scheduling scheme to broadcast Invalidation Report (IR) in order to reduce query delay and communication overhead to maintain strong data consistency. The simulation result shows that our proposed method has a strength in terms of query delay and communication overhead.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.306-306
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• 2017

Screening for drought and salinity tolerance was undertaken for selected Philippine upland rice landraces during germinative and seedling stages to identify varieties which can potentially be grown in marginally dry and saline soils. While increasing PEG and NaCl concentrations caused obvious signs of injury to all rice genotypes, considerable varietal differences were noted in the nature of responses providing evidence that these genotypes possess broad intraspecific genetic variations for drought and salt tolerance. Inconsistent responses of these varieties during both growth stages highlight complexities involved in stress responses and underscore the futility of utilizing a single stage in the rice plant's life cycle for physiological screening. Notwithstanding these perplexing responses, G_Katiil and Ml-Pilit Tapul were observed to thrive relatively well despite increased salt and drought stress during early growth stages and may therefore possess genes needed in crop improvement efforts for drought and salinity tolerance. While these results do not reflect the entire spectrum of adaptive expression to drought and salinity stress during the life cycle of the upland rice plant, they nonetheless provide an easy, reliable and reproducible method for preliminary identification of drought and salt tolerant rice varieties.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.131-143
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• 2007

The current trends in information technology and intelligent systems use data mining techniques to discover patterns and extract rules from distributed databases. In distributed environment, the extracted rules from data mining techniques can be used in dynamic replications, adaptive load balancing and other schemes. However, transmission of large data through the system can cause errors and unreliable results. This paper proposes the intelligent distributed platform based on dynamic group binding using mobile agents which addresses the use of intelligence in distributed environment. The proposed grouping service implements classification scheme of objects. Data compressor agent and data miner agent extracts rules and compresses data, respectively, from the service node databases. The proposed algorithm performs preprocessing where it merges the less frequent dataset using neuro-fuzzy classifier before sending the data. Object group classification, data mining the service node database, data compression method, and rule extraction were simulated. Result of experiments in efficient data compression and reliable rule extraction shows that the proposed algorithm has better performance compared to other methods.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.272-275
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• 2014

An improved fuzzy Kalman filtering speed estimation scheme was proposed by means of measuring stator side voltage and current value based on vector control state equation of induction motor. The designed fuzzy adaptive controller conducted recursive online correction of measurement noise covariance matrix by monitoring the ratio of theory residuals and actual residuals to make it approach real noise level gradually, allowing the filter to perform optimal estimation to improve estimation accuracy of EKF. Meanwhile, co-simulation scheme based on MATLAB and Ansoft was proposed in order to improve simulation accuracy. Field-circuit coupling problems of induction motor under the action of vector control were solved and the parameter optimization accuracy was improved dramatically. The simulation and experimental results show that this algorithm has a strong ability to inhibit the random measurement noise. It is able to estimate motor speed accurately, and has superior static and dynamic characteristics.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.2
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• pp.38-49
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• 2001

This paper presents a new voltage down converter(VDC) using charge and discharge current adjustment circuitry that provides high frequency application. This VDC consist of a common driving circuit and compensation circuits: 2 sensors and each driving transistors for controlling gate current of driving transistor. These sensors are operated as adaptive biasing method with high speed and low power consumption. This circuit is designed with a $0.62{\mu}m$ N well CMOS technology. In H-spice simulation results, internal voltage is bounded ( IV, +0.6V) in proposed circuitry when load current rapidly increases and decreases during Gns between 0 and $200m{\Lambda}$. And the recovery time of internal voltage is about 7ns and 10ns when load current increases and decreases respectively. That is fast better than common driving circuit. Total power consumption is about 1.2mW.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.76-84
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• 2007

The goal of this paper is to improve the watermarking performance using the following two methods; watermark estimation and low density parity check (LDPC) codes. For a blind watermark decoding, the power of a host image, which is hundreds times greater than the watermark power, is the main noise source. Therefore, a technique that can reduce the effect of the power of the host image to the detector is required. To this end, we need to estimate watermark from the watermarked image. In this paper, the watermark estimation is done by an adaptive estimation method with the generalized Gaussian distribution modeling of sub-band coefficients in the wavelet domain. Since the watermark capacity as well as the error rate can be improved by adopting optimum decoding principles and error correcting codes (ECC), we employ the LDPC codes for the decoding of the estimated watermark. Also, in LDPC codes, the knowledge about the noise power can improve the error correction capability. Simulation results demonstrate the superior performance of the proposed algorithm comparing to LDPC decoding with other estimation-based watermarking algorithms.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.80-91
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• 2010

Due to the convergence of broadcasting and communications, IPTV services are spotlighted as the that next-generation multimedia services. IPTV services should have functionality such as unlimited channel capacity, extension of media, QoS awareness and are required increasing traffic and quality control technology to adapt the attributes of IPTV service. Consequently, flow based quality control techniques are needed. Therefore, many studies for providing Internet QoS are performed at IETF (Internet Engineering Task Force). As the buffer management mechanism among IP QoS methods, active queue management method such as RED(Random Early Detection) and modified RED algorithms have proposed. However, these algorithms have difficulties to satisfy the requirements of various Internet user QoS. Therefore, in this paper we propose the Flow based AQM(Active Queue Management) algorithm for the multimedia services that request various QoS requirements. The proposed algorithm can converge the packet loss ratio to the target packet loss ratio of required QoS requirements. And we present a performance evaluation by the simulations using the ns-2.